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Abstract:

Systems and methods for processing sensor data are provided. In some
embodiments, systems and methods are provided for calibration of a
continuous analyte sensor. In some embodiments, systems and methods are
provided for classification of a level of noise on a sensor signal. In
some embodiments, systems and methods are provided for determining a rate
of change for analyte concentration based on a continuous sensor signal.
In some embodiments, systems and methods for alerting or alarming a
patient based on prediction of glucose concentration are provided.

Claims:

1. A method for classifying a level of noise in a signal obtained from a
continuous glucose sensor based on a signal strength, the method
comprising: receiving a signal from a continuous glucose sensor during a
sensor session; classifying a level of noise in the signal during the
sensor session based on one or more noise thresholds, wherein the one or
more noise thresholds are adaptively determined during the sensor
session; and controlling an output responsive to the noise
classification.

2. The method of claim 1, further comprising determining a signal
strength of the signal from the continuous glucose sensor during the
sensor session.

3. The method of claim 2, wherein the determining a signal strength
comprises applying a low pass filter to the signal to determine the
signal strength of the signal.

4. The method of claim 2, wherein the classifying comprises adaptively
determining the one or more noise thresholds for classification of the
level of noise in the signal, wherein the one or more noise thresholds
are based the signal strength of the signal.

5. The method of claim 2, wherein the one or more noise thresholds are
based on a percentage of the signal strength.

6. The method of claim 1, wherein the classifying comprises applying one
or more low pass filters to a noise signal to obtain one or more noise
indicators and comparing the one or more noise indicators with the one or
more noise thresholds.

7. The method of claim 1, further comprising determining a noise signal
from the sensor signal, wherein the classifying comprises applying one or
more filters to the noise signal to obtain one or more noise indicators
and comparing the one or more noise indicators with one or more noise
thresholds.

8. The method of claim 1, wherein the controlling comprises outputting
information indicative of the noise classification to an external device.

9. The method of claim 1, wherein the controlling comprises displaying
information indicative of the noise classification on a user interface.

10. The method of claim 1, wherein the controlling comprises decision
making for at least one of displaying, calibrating, alarming, sensor
diagnostics or insulin delivery.

11. The method of claim 1, wherein the controlling comprises controlling
a display of rate of change information.

12. The method of claim 1, wherein the controlling comprises controlling
an alarm indicative of at least one of hypoglycemia, hyperglycemia,
predicted hypoglycemia, or predicted hyperglycemia.

14. A system for classifying a level of noise in a signal obtained from a
continuous glucose sensor, the system comprising: a continuous glucose
sensor configured to provide a signal indicative of a glucose
concentration in a host; and a computer system configured to receive the
signal from the continuous glucose sensor during a sensor session, to
classify a level of noise in the signal during the sensor session based
on one or more noise thresholds, and to control an output responsive to
the noise classification, wherein the one or more noise thresholds are
adaptively determined during the sensor session.

15. The system of claim 14, wherein the computer system is configured to
determine a signal strength of the signal from the continuous glucose
sensor during the sensor session.

16. The system of claim 15, wherein the computer system is configured to
determine a signal strength by applying a low pass filter to the signal.

17. The system of claim 15, wherein the computer system is configured to
adaptively determine the one or more noise thresholds for classification
of the level of noise in the signal based on the signal strength of the
signal.

18. The system of claim 15, wherein the computer system is configured to
adaptively determine the one or more noise thresholds for classification
of the level of noise on the signal based on a percentage of the signal
strength.

19. The system of claim 14, wherein the computer system is configured to
apply one or more low pass filters to a noise signal to obtain one or
more noise indicators and to compare the one or more noise indicators
with the one or more noise thresholds.

20. The system of claim 14, wherein the computer system is configured to
determine a noise signal from the sensor signal by applying one or more
filters to the noise signal to obtain one or more noise indicators and
comparing the one or more noise indicators with one or more noise
thresholds.

21. The system of claim 14, wherein the output comprises information
indicative of the noise classification.

22. The system of claim 14, wherein the computer system comprises a user
interface, and wherein the output is displayed via the user interface.

23. The system of claim 14, wherein the computer system is configured to
control the output by making decisions for at least one of displaying,
calibrating, alarming, sensor diagnostics or insulin delivery.

24. The system of claim 14, wherein the computer system is configured to
control an output by controlling a display of rate of change information.

25. The system of claim 14, wherein the computer system is configured to
control an output by controlling an alarm indicative of at least one of
hypoglycemia, hyperglycemia, predicted hypoglycemia, or predicted
hyperglycemia.

26. The system of claim 14, wherein the computer system is configured to
control an output by controlling control medicament therapy instructions.

Description:

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is a continuation of U.S. application Ser. No.
12/258,318 filed Oct. 24, 2008, which claims the benefit of U.S.
Provisional Application No. 61/014,398 filed Dec. 17, 2007, each of which
is incorporated by reference herein in its entirety, and each of which is
hereby expressly made a part of this specification.

FIELD OF THE INVENTION

[0002] The present invention relates generally to systems and methods for
processing data received from an analyte sensor, such as a glucose
sensor.

BACKGROUND OF THE INVENTION

[0003] Diabetes mellitus is a disorder in which the pancreas cannot create
sufficient insulin (Type I or insulin dependent) and/or in which insulin
is not effective (Type 2 or non-insulin dependent). In the diabetic
state, the victim suffers from high blood sugar, which causes an array of
physiological derangements (kidney failure, skin ulcers, or bleeding into
the vitreous of the eye) associated with the deterioration of small blood
vessels. A hypoglycemic reaction (low blood sugar) is induced by an
inadvertent overdose of insulin, or after a normal dose of insulin or
glucose-lowering agent accompanied by extraordinary exercise or
insufficient food intake.

[0004] Conventionally, a diabetic person carries a self-monitoring blood
glucose (SMBG) monitor, which typically requires uncomfortable finger
pricking methods. Due to the lack of comfort and convenience, a diabetic
will normally only measure his or her glucose levels two to four times
per day. Unfortunately, these time intervals are spread so far apart that
the diabetic will likely find out too late, sometimes incurring dangerous
side effects, of a hyperglycemic or hypoglycemic condition. In fact, it
is not only unlikely that a diabetic will take a timely SMBG value, but
additionally the diabetic will not know if his blood glucose value is
going up (higher) or down (lower) based on conventional methods.

[0005] Consequently, a variety of glucose sensors are being developed for
continuously detecting and/or quantifying blood glucose values. Many
implantable glucose sensors suffer from complications within the body and
provide only short-term and less-than-accurate sensing of blood glucose.
Similarly, transdermal sensors have run into problems in accurately
sensing and reporting back glucose values continuously over extended
periods of time. Some efforts have been made to obtain blood glucose data
from implantable devices and retrospectively determine blood glucose
trends for analysis; however these efforts do not aid the diabetic in
determining real-time blood glucose information. Some efforts have also
been made to obtain blood glucose data from transdermal devices for
prospective data analysis, however similar problems have occurred.

SUMMARY OF THE INVENTION

[0006] In a first aspect, a method for calibrating an analyte sensor is
provided, the method comprising: receiving sensor data from an analyte
sensor, the sensor data comprising at least one sensor data point;
receiving reference data from a reference analyte monitor, the reference
data comprising at least one reference analyte value; matching at least
one sensor data point with at least one reference analyte value to form a
matched data pair; converting sensor data into at least one estimated
analyte value, utilizing the matched data pair; and displaying the
estimated analyte value within about 10 minutes of receiving the
reference analyte value.

[0007] In an embodiment of the first aspect, the step of displaying the
estimated analyte value is performed within about 5 minutes of receiving
the reference analyte value.

[0008] In an embodiment of the first aspect, the step of displaying the
estimated analyte value is performed within about 1 minute of receiving
the reference analyte value.

[0009] In a second aspect, a method for calibrating glucose sensor data
from a continuous glucose sensor is provided, the method comprising:
obtaining a reference glucose value; and matching the reference glucose
value with a sensor glucose value without compensating for a time lag
between the reference glucose value and the sensor glucose value such
that a time stamp for the reference glucose value is as close as possible
to a time stamp of the sensor glucose value.

[0010] In an embodiment of the second aspect, the time stamp of the
reference glucose value is within about 5 minutes of the time stamp of
the sensor glucose value.

[0011] In an embodiment of the second aspect, the time lag between the
reference glucose value and the sensor glucose value is determined, at
least in part, by a filter applied to raw glucose sensor data measured by
the continuous glucose sensor.

[0012] In a third aspect, a method for calibrating glucose sensor data
from a continuous glucose sensor is provided, the method comprising:
immediately calibrating a continuous glucose sensor by matching a
reference glucose value with a first sensor glucose value; and
subsequently calibrating the continuous glucose sensor by matching the
reference glucose value with a second sensor glucose value.

[0013] In an embodiment of the third aspect, the first sensor glucose
value and the second sensor glucose value are different.

[0014] In an embodiment of the third aspect, the step of immediately
calibrating comprises matching the reference glucose value with the first
sensor glucose value without compensating for a time lag.

[0015] In an embodiment of the third aspect, the step of subsequently
calibrating comprises matching the reference glucose value with the
second sensor glucose value, whereby a time lag is compensated for.

[0016] In an embodiment of the third aspect, the step of immediately
calibrating further comprises determining a calibration state comprising
one of an out-of-calibration state and an in-calibration state.

[0017] In an embodiment of the third aspect, the step of determining a
calibration state further comprises displaying information indicative of
the calibration state on a user interface.

[0018] In an embodiment of the third aspect, the method further comprises
displaying immediately calibrated sensor data after the step of
immediately calibrating.

[0019] In an embodiment of the third aspect, the method further comprises
displaying subsequently calibrated sensor data after the step of
subsequently calibrating.

[0020] In a fourth aspect, a system for calibrating an analyte sensor is
provided, the system comprising: a sensor data module configured to
receive sensor data from an analyte sensor, the sensor data comprising at
least one sensor data point; a reference input module, configured to
receive reference data from a reference analyte monitor, the reference
data comprising at least one reference analyte value; a processor module
configured to match at least one sensor data point with at least one
reference analyte value to form a matched data pair, wherein the
processor module is further configured to convert sensor data into at
least one estimated analyte value utilizing the matched data pair; and an
output module configured to display the estimated analyte value within
about 10 minutes of receiving the reference analyte value.

[0021] In a fifth aspect, a system is provided for calibrating glucose
sensor data from a continuous glucose sensor, the system comprising: a
processor module configured to obtain a reference glucose value and match
the reference glucose value with a sensor glucose value without
compensating for a time lag such that a time stamp of the reference
glucose value is as close as possible to a time stamp of the sensor
glucose value.

[0022] In a sixth aspect, a system is provided for calibrating glucose
sensor data from a continuous glucose sensor, the system comprising: a
processor module configured to immediately calibrate a continuous glucose
sensor by matching a reference glucose value with a first sensor glucose
value and subsequently calibrating the continuous glucose sensor by
matching the reference glucose value with a second sensor glucose value.

[0023] In a seventh aspect, a system is provided for processing data from
a continuous analyte sensor, comprising: a continuous analyte sensor
configured to continuously measure a concentration of analyte in a host
and provide continuous analyte sensor data associated therewith; and a
processor module configured to set a mode selected from a plurality of
predetermined modes, wherein the processor module is configured to
process the continuous analyte sensor data based at least in part on the
mode.

[0024] In an embodiment of the seventh aspect, the processor module is
configured to set the mode at least in part responsive to receipt of a
user input.

[0025] In an embodiment of the seventh aspect, the system comprises one or
more buttons, and wherein the processor module is configured to receive
the user input by selection of one or more buttons.

[0026] In an embodiment of the seventh aspect, the system comprises a
screen, wherein the processor module is configured to display one or more
menus on the screen, and wherein the processor module is configured to
receive the user input by selection of one or more items from the one or
more menus.

[0027] In an embodiment of the seventh aspect, the system is configured to
operably connect with an external system such that data can be
transmitted from the external system to the system, and wherein the
processor module is configured to receive the user input by a data
transmission received from the external system.

[0028] In an embodiment of the seventh aspect, the operable connection is
a wired connection.

[0029] In an embodiment of the seventh aspect, the operable connection is
a wireless connection.

[0030] In an embodiment of the seventh aspect, the external system
comprises a programming configured to schedule events on a calendar.

[0031] In an embodiment of the seventh aspect, the processor module is
configured to automatically set the mode based at least in part on a
comparison of data with one or more criteria.

[0032] In an embodiment of the seventh aspect, the system further
comprises an accelerometer, wherein the data comprises data received from
the accelerometer.

[0033] In an embodiment of the seventh aspect, the system further
comprises a temperature sensor, wherein the data comprises data received
from the temperature sensor.

[0034] In an embodiment of the seventh aspect, the continuous analyte
sensor comprises a continuous glucose sensor, wherein the data comprises
glucose sensor data, and wherein the one or more criteria comprise one or
more thresholds associated with hyperglycemia and/or hypoglycemia.

[0035] In an embodiment of the seventh aspect, the processor module
comprises programming configured to automatically set the mode at least
in part responsive to an adaptive mode learning module, wherein the
adaptive mode learning module is configured to process sensor data and
time-corresponding mode over time and subsequently modify the automatic
mode-setting programming at least in part responsive thereto.

[0036] In an embodiment of the seventh aspect, the system is further
configured to provide an alarm responsive to the sensor data meeting one
or more criteria.

[0037] In an embodiment of the seventh aspect, the one or more criteria
are based at least in part on the mode.

[0038] In an embodiment of the seventh aspect, the system is configured to
provide the alarm via an audible sound, visual display, vibration,
alphanumeric message, and/or wireless transmission based on the mode.

[0039] In an embodiment of the seventh aspect, the system is further
configured to determine a therapy instruction based at least in part on
the mode.

[0040] In an embodiment of the seventh aspect, the system is further
configured to determine the therapy instruction based at least on the
continuous analyte sensor data.

[0041] In an embodiment of the seventh aspect, the system is operably
connected with a medicament delivery module, and wherein the medicament
delivery module comprises programming configured to require a validation
of the therapy instruction prior to delivery of the therapy via the
medicament delivery module, and wherein validation requirement is
dependent upon the mode.

[0042] In an embodiment of the seventh aspect, the processor module is
further configured to classify a level of noise in the continuous analyte
sensor data

[0043] In an embodiment of the seventh aspect, the processor module is
configured to set the mode responsive at least in part to the level of
noise.

[0044] In an embodiment of the seventh aspect, the plurality of
predetermined modes include two or more modes selected from the group
consisting of resting mode, do not disturb mode, exercise mode, illness
mode, menstruation mode, mealtime mode, day mode, night mode,
hypoglycemic mode, hyperglycemic mode, noise mode.

[0045] In an embodiment of the seventh aspect, the processor module
further comprises a timer associated with one or more modes, wherein the
timer is configured to set the mode for a predetermined amount of time.

[0046] In an embodiment of the seventh aspect, the timer is user settable.

[0047] In an embodiment of the seventh aspect, the processor module is
further configured to set the mode at least in part responsive to a mode
profile.

[0048] In an embodiment of the seventh aspect, the mode profile is user
settable.

[0049] In an eighth aspect, a method for processing data from a continuous
analyte sensor is provided, comprising: receiving continuous analyte
sensor data from a continuous analyte sensor; setting a mode selected
from a plurality of predetermined modes; and processing the continuous
analyte sensor data based at least in part on the mode.

[0050] In an embodiment of the eighth aspect, the step of setting the mode
comprises receiving a user input.

[0051] In an embodiment of the eighth aspect, the step of receiving user
input comprises receiving a selection of one or more buttons.

[0052] In an embodiment of the eighth aspect, the step of receiving user
input comprises receiving a selection of one or more items from the one
or more menus displayed on a screen.

[0053] In an embodiment of the eighth aspect, the step of receiving user
input comprises receiving a data transmission from an external system.

[0054] In an embodiment of the eighth aspect, the step of receiving a data
transmission comprises receiving a wired data transmission.

[0055] In an embodiment of the eighth aspect, the step of receiving a data
transmission comprises receiving a wireless data transmission.

[0056] In an embodiment of the eighth aspect, the step of receiving a data
transmission comprises receiving a data transmission from an event
scheduling software.

[0057] In an embodiment of the eighth aspect, the step of processing
comprises comparing the continuous analyte sensor data with one or more
criteria.

[0058] In an embodiment of the eighth aspect, the method further comprises
receiving data from an accelerometer, wherein the step of setting the
mode is responsive at least in part to the data received from an
accelerometer.

[0059] In an embodiment of the eighth aspect, the method further comprises
receiving data from a temperature sensor, wherein the step of setting the
mode is responsive at least in part to the received data.

[0060] In an embodiment of the eighth aspect, the step of receiving
continuous analyte sensor data comprises receiving data from a continuous
glucose sensor and wherein the one or more criteria comprise one or more
thresholds associated with hypoglycemia and/or hyperglycemia.

[0061] In an embodiment of the eighth aspect, the step of setting the mode
comprises automatically setting the mode.

[0062] In an embodiment of the eighth aspect, the method further comprises
processing sensor data and time-corresponding mode settings over time and
wherein the step of automatically setting the mode is based at least in
part on one or more modes.

[0063] In an embodiment of the eighth aspect, the step of processing
comprises initiating an alarm responsive to the sensor data meeting one
or more criteria.

[0064] In an embodiment of the eighth aspect, the one or more criteria are
based at least in part on the mode.

[0065] In an embodiment of the eighth aspect, the alarm comprises an
audible alarm, visual alarm, vibration alarm, alphanumeric message alarm,
and/or wireless transmission alarm based at least in part on the mode.

[0066] In an embodiment of the eighth aspect, the step of processing
comprises determining a therapy instruction based at least in part on the
mode.

[0067] In an embodiment of the eighth aspect, the step of determining the
therapy instruction is based at least in part on the continuous analyte
sensor data.

[0068] In an embodiment of the eighth aspect, the step of determining a
therapy instruction further comprises requiring a validation of the
therapy instruction prior to delivery of the therapy via the medicament
delivery device, and wherein validation is based at least in part on the
mode.

[0069] In an embodiment of the eighth aspect, the step of processing
comprises classifying a level of noise in the continuous analyte sensor
data.

[0070] In an embodiment of the eighth aspect, the step of classifying a
level of noise comprises setting the mode based at least in part on the
level of noise.

[0071] In an embodiment of the eighth aspect, the step of setting a mode
from a plurality of predetermined modes comprise two or more modes
selected from the group consisting of resting mode, do not disturb mode,
exercise mode, illness mode, menstruation mode, mealtime mode, day mode,
night mode, hypoglycemic mode, hyperglycemic mode, noise mode.

[0072] In an embodiment of the eighth aspect, the step of setting the mode
comprises setting a mode for a predetermined amount of time based at
least in part upon a timer.

[0073] In an embodiment of the eighth aspect, the timer is user settable.

[0074] In an embodiment of the eighth aspect, the step of setting the mode
comprises setting the mode based at least in part on a mode profile.

[0075] In an embodiment of the eighth aspect, setting the mode profile is
user settable.

[0076] In a ninth aspect, a method is provided for processing of a
continuous glucose sensor signal, the method comprising: receiving sensor
data from a continuous analyte sensor, including one or more sensor data
points; comparing sensor data against one or more criteria for at least
one of hypoglycemia, hyperglycemia, predicted hypoglycemia, and predicted
hyperglycemia; and triggering an alarm when the sensor data meets one or
more predetermined criteria.

[0077] In an embodiment of the ninth aspect, the alarm comprises first and
second user selectable alarms.

[0078] In an embodiment of the ninth aspect, the first alarm is configured
to alarm during a first time of day and wherein the second alarm is
configured to alarm during a second time of day.

[0079] In an embodiment of the ninth aspect, the alarm is configured to
turn on a light.

[0080] In an embodiment of the ninth aspect, the alarm is configured to
alarm a remote device.

[0081] In an embodiment of the ninth aspect, the alarm comprises sending a
text message to a remote device.

[0082] In a tenth aspect, a system is provided for processing continuous
glucose sensor data, the system comprising: a continuous glucose sensor
configured to generate sensor data associated with a glucose
concentration in a host; and a computer system that compares sensor data
against predetermined criteria for at least one of hypoglycemia,
hyperglycemia, predicted hypoglycemia and predicted hyperglycemia, and
triggers an alarm when the sensor data meets predetermined criteria.

[0083] In an embodiment of the tenth aspect, the alarm comprises first and
second user selectable alarms.

[0084] In an embodiment of the tenth aspect, the first alarm is configured
to alarm during a first time of day and wherein the second alarm is
configured to alarm during a second time of day.

[0085] In an embodiment of the tenth aspect, the alarm is configured to
turn a light on.

[0086] In an embodiment of the tenth aspect, the alarm is configured to
alarm a remote device located more than about 10 feet away from the
continuous glucose sensor.

[0087] In an embodiment of the tenth aspect, the alarm comprises a text
message, and wherein the computer system is configured to send the text
message a remote device.

[0088] In an eleventh aspect, a method for processing continuous glucose
sensor data is provided, the method comprising: receiving sensor data
from a continuous glucose sensor, wherein the sensor data comprises one
or more sensor data points; obtaining an estimated sensor glucose value
from the one or more sensor data points; calculating at least two rate of
change values; and filtering the at least two rate of change values to
obtain a filtered rate of change value.

[0089] In an embodiment of the eleventh aspect, the at least two rate of
change values are point-to-point rate of change values.

[0090] In an embodiment of the eleventh aspect, the method further
comprises determining a predicted value for a future time period based on
the estimated sensor glucose value, the filtered rate of change value,
and a time to the future time period.

[0091] In an embodiment of the eleventh aspect, the time to the future
time period is user selectable.

[0092] In an embodiment of the eleventh aspect, the method further
comprises comparing the predicted value against a threshold.

[0093] In an embodiment of the eleventh aspect, the method further
comprises triggering an alarm when the predicted value passes the
threshold.

[0094] In an embodiment of the eleventh aspect, the threshold is user
selectable.

[0095] In an embodiment of the eleventh aspect, the method further
comprises determining a predicted time to a threshold, wherein the
predicted time is based at least in part on the estimated sensor glucose
value, the filtered rate of change value, and the threshold.

[0096] In an embodiment of the eleventh aspect, the threshold is user
selectable.

[0097] In an embodiment of the eleventh aspect, the method further
comprises displaying the predicted time to the threshold on a user
interface.

[0098] In an embodiment of the eleventh aspect, the step of displaying the
predicted time to the threshold is performed only when the predicted time
is below a predetermined value

[0099] In an embodiment of the eleventh aspect, the method further
comprises determining an insulin therapy based at least in part on the
filtered rate of change value.

[0100] In an embodiment of the eleventh aspect, the step of filtering to
obtain a filtered rate of change value is performed continuously.

[0101] In an embodiment of the eleventh aspect, the step of filtering to
obtain a filtered rate of change value is not performed when a level of
noise is above a threshold.

[0102] In an embodiment of the eleventh aspect, the method further
comprises displaying a trend arrow representative of the filtered rate of
change values.

[0103] In a twelfth aspect, a system is provided for processing continuous
glucose sensor data, the system comprising: a continuous glucose sensor
configured to generate sensor data associated with glucose concentration
in a host; and a computer system that obtains an estimated sensor glucose
value, calculates at least two rate of change values, and filters the at
least two rate of change values to obtain a filtered rate of change
value.

[0104] In an embodiment of the twelfth aspect, the at least two rate of
change values are point-to-point rate of change values.

[0105] In an embodiment of the twelfth aspect, the computer system
determines a predicted value for a future time period based on the
estimated sensor glucose value, the filtered rate of change value and a
time to the future time period.

[0106] In an embodiment of the twelfth aspect, the time to the future time
period is user selectable.

[0107] In an embodiment of the twelfth aspect, the computer system
compares the predicted value against a threshold.

[0108] In an embodiment of the twelfth aspect, the computer system
triggers an alarm when the predicted value passes the threshold.

[0109] In an embodiment of the twelfth aspect, the threshold is user
selectable.

[0110] In an embodiment of the twelfth aspect, the computer system
determines a predicted time to a threshold, wherein the predicted time is
based at least in part on the estimated sensor glucose value, the
filtered rate of change value, and a threshold

[0111] In an embodiment of the twelfth aspect, the threshold is user
selectable.

[0112] In an embodiment of the twelfth aspect, the computer system is
configured to display the predicted time to threshold on a user
interface.

[0113] In an embodiment of the twelfth aspect, the computer system is
configured to display the predicted time to threshold only when the
predicted time is below a predetermined value.

[0114] In an embodiment of the twelfth aspect, the computer system
determines an insulin therapy based at least in part on the filtered rate
of change value.

[0115] In an embodiment of the twelfth aspect, the computer system
continuously filters the at least two rate of change values to obtain a
filtered rate of change value.

[0116] In an embodiment of the twelfth aspect, the computer system
displays a trend arrow representative of the filtered rate of change
values.

[0117] In an embodiment of the twelfth aspect, the computer system filters
the at least two rate of change values to obtain a filtered rate of
change value only when a level of noise is below a threshold.

[0118] In a thirteenth aspect, a method for determining a rate of change
of a continuous glucose sensor signal is provided, comprising: receiving
sensor data from a continuous analyte sensor, the sensor data comprising
one or more sensor data points; and calculating a rate of change for a
window of sensor data, wherein the window of sensor data comprises two or
more sensor data points.

[0119] In an embodiment of the thirteenth aspect, the window of sensor
data comprises a user selectable time period.

[0120] In an embodiment of the thirteenth aspect, the window of sensor
data comprises a programmable time period.

[0121] In an embodiment of the thirteenth aspect, the window of sensor
data adaptively adjusts based at least in part on a level of noise in the
sensor data.

[0122] In a fourteenth aspect, a system is provided for determining a rate
of change of a continuous glucose sensor signal, comprising: a continuous
glucose sensor configured to generate sensor data associated with a
glucose concentration in a host; and a computer system configured to
calculate a rate of change for a window of sensor data, the sensor data
comprising two or more sensor data points.

[0123] In an embodiment of the fourteenth aspect, the window of sensor
data is a user selectable time period.

[0124] In an embodiment of the fourteenth aspect, the window of sensor
data is a programmable time period.

[0125] In an embodiment of the fourteenth aspect, the computer system is
configured to adaptively adjust the window of sensor data based at least
in part on a level of noise in the sensor data.

[0126] In a fifteenth aspect, a method is provided for determining a rate
of change of a continuous glucose sensor signal, comprising: receiving
sensor data from a continuous analyte sensor; determining a level of
noise in the sensor data; and calculating a rate of change for a window
of the sensor data, wherein the window of sensor data comprises two or
more sensor data points.

[0127] In an embodiment of the fifteenth aspect, the step of calculating a
rate of change uses either raw sensor data or filtered sensor data,
depending at least in part upon the level of noise determined.

[0128] In an embodiment of the fifteenth aspect, the step of calculating a
rate of change comprises at least two rate of change calculations, and
wherein the step of calculating a rate of change further comprises
adaptively selecting a filter to apply to the at least two rate of change
calculations based at least in part on the level of noise determined.

[0129] In a sixteenth aspect, a system is provided for determining a rate
of change of a continuous glucose sensor signal, comprising: a continuous
glucose sensor configured to generate sensor data associated with a
glucose concentration in a host; and a computer system configured to
determine a level of noise in the sensor data and calculate a rate of
change for a window of the sensor data, wherein the window of sensor data
comprises two or more sensor data points.

[0130] In an embodiment of the sixteenth aspect, the computer system is
configured to use either raw sensor data or filtered sensor data in the
rate of change calculation depending at least in part upon the level of
noise determined.

[0131] In an embodiment of the sixteenth aspect, the rate of change
calculation comprises calculating at least two rate of change
calculations, and wherein the rate of change calculation further
comprises adaptively selecting a filter to apply to the rate of change
calculation based at least in part on the level of noise determined.

[0132] In a seventeenth aspect, a method is provided for classifying a
level of noise in a signal obtained from a continuous glucose sensor, the
method comprising: receiving a signal from a continuous glucose sensor;
and classifying a level of noise on the signal.

[0133] In an embodiment of the seventeenth aspect, the step of classifying
comprises applying a low pass filter to the signal to determine a signal
strength.

[0134] In an embodiment of the seventeenth aspect, the step of classifying
comprises defining one or more noise thresholds for classification of the
level of noise on the signal, wherein the one or more noise thresholds
are based at least in part on a percentage of the signal strength.

[0135] In an embodiment of the seventeenth aspect, the step of classifying
comprises applying one or more low pass filters to the noise signal to
obtain one or more noise indicators and comparing the noise indicators
with the one or more noise thresholds.

[0136] In an embodiment of the seventeenth aspect, the method further
comprises determining a noise signal from the sensor signal, and wherein
the step of classifying comprises applying one or more filters to the
noise signal to obtain one or more noise indicators and comparing the
noise indicators with one or more noise thresholds.

[0137] In an embodiment of the seventeenth aspect, the step of classifying
comprises performing spectral analysis to determine at least one of a
signal strength and a noise indicator.

[0138] In an embodiment of the seventeenth aspect, the step of classifying
a level of noise comprises using hysteresis.

[0139] In an embodiment of the seventeenth aspect, the method further
comprises controlling an output based at least in part on the level of
noise.

[0140] In an embodiment of the seventeenth aspect, the method further
comprises controlling a display based at least in part on the level of
noise.

[0141] In an embodiment of the seventeenth aspect, the step of controlling
a display comprises controlling the display of raw and/or filtered data
based at least in part on the level of noise.

[0142] In an embodiment of the seventeenth aspect, the step of controlling
a display comprises displaying rate of change information based at least
in part on the level of noise.

[0143] In an embodiment of the seventeenth aspect, the method further
comprises a step of controlling at least one alarm indicative of at least
one of hypoglycemia, hyperglycemia, predicted hypoglycemia, and predicted
hyperglycemia based at least in part on the level of noise.

[0144] In an embodiment of the seventeenth aspect, the method further
comprises a step of controlling medicament delivery and/or medicament
therapy instructions based at least in part on the level of noise.

[0145] In an embodiment of the seventeenth aspect, the method further
comprises a step of diagnosing a sensor condition based at least in part
on the level of noise.

[0146] In an embodiment of the seventeenth aspect, the method further
comprises a step of suspending display of sensor data based at least in
part on the level of noise.

[0147] In an embodiment of the seventeenth aspect, the method further
comprises a step of shutting down a sensor session based at least in part
on the level of noise.

[0148] In an embodiment of the seventeenth aspect, the method further
comprises a step of displaying the level of noise on the user interface.

[0149] In an embodiment of the seventeenth aspect, the method further
comprises a step of displaying information indicative of the level of
noise on the sensor signal.

[0150] In an embodiment of the seventeenth aspect, the method further
comprises a step of displaying information indicative of an amount of
time that the signal has been classified as having a level of noise.

[0151] In an eighteenth aspect, a system is provided for classifying a
level of noise in a signal obtained from a continuous glucose sensor, the
system comprising: a continuous glucose sensor that provides a signal
indicative of a glucose concentration in a host; and a computer system
that classifies a level of noise on the signal.

[0152] In an embodiment of the eighteenth aspect, the method further
comprises the computer system filters the signal by applying a low pass
filter to the signal to determine a signal strength.

[0153] In an embodiment of the eighteenth aspect, the computer system
defines one or more noise thresholds for classification of the level of
noise on the signal, and wherein the one or more noise thresholds are
based at least in part on a percentage of the signal strength.

[0154] In an embodiment of the eighteenth aspect, the computer system
classifies the level of noise by applying one or more low pass filters to
the noise signal to obtain one or more noise indicators and comparing the
noise indicators to the one or more noise thresholds.

[0155] In an embodiment of the eighteenth aspect, the computer system
classifies a level of noise by applying one or more filters to the noise
signal to obtain one or more noise indicators and comparing the noise
indicators with one or more noise thresholds.

[0156] In an embodiment of the eighteenth aspect, the computer system
classifies a level of noise by performing spectral analysis to determine
at least one of a signal strength and a noise indicator.

[0157] In an embodiment of the eighteenth aspect, the computer system uses
hysteresis to classify the level of noise.

[0158] In an embodiment of the eighteenth aspect, the computer system
provides an output based at least in part on the level of noise.

[0159] In an embodiment of the eighteenth aspect, the system further
comprises a user interface configured to display the glucose
concentration to the host, wherein the computer system is configured to
control the display based at least in part on the level of noise.

[0160] In an embodiment of the eighteenth aspect, the computer system
controls the display of raw and/or filtered data based at least in part
on the level of noise.

[0161] In an embodiment of the eighteenth aspect, the computer system
controls the display of rate of change information based at least in part
on the level of noise.

[0162] In an embodiment of the eighteenth aspect, the computer system is
configured to control alarms indicative of at least one of hypoglycemia,
hyperglycemia, predicted hypoglycemia, and predicted hyperglycemia based
at least in part on the level of noise.

[0163] In an embodiment of the eighteenth aspect, the computer system is
configured to control medicament delivery and/or medicament therapy
instructions based at least in part on the level of noise.

[0164] In an embodiment of the eighteenth aspect, the computer system is
configured to diagnose a sensor condition based at least in part on the
level of noise.

[0165] In an embodiment of the eighteenth aspect, the computer system is
configured to suspend display of a glucose concentration based at least
in part on the level of noise.

[0166] In an embodiment of the eighteenth aspect, the computer system is
configured to shut down a sensor session based at least in part on the
level of noise.

[0167] In an embodiment of the eighteenth aspect, the computer system is
configured to display the level of noise on the user interface.

[0168] In an embodiment of the eighteenth aspect, the computer system is
configured to display information indicative of the level of noise on the
sensor signal.

[0169] In an embodiment of the eighteenth aspect, the computer system is
configured to display information indicative of an amount of time the
signal has been classified as having a level of noise.

[0170] In a nineteenth embodiment, a method is provided for calibration of
a continuous glucose sensor, the method comprising: receiving sensor data
from a continuous analyte sensor, the sensor data comprising one or more
sensor data points; receiving and processing calibration information;
evaluating a predictive accuracy of calibration information; and
determining when to request reference data based at least in part on the
predictive accuracy of calibration information.

[0171] In an embodiment of the nineteenth aspect, the step of determining
when to request reference data comprises determining a time period.

[0172] In an embodiment of the nineteenth aspect, the time period is
between about 0 minutes and 7 days.

[0173] In an embodiment of the nineteenth aspect, the step of receiving
and processing calibration information comprises receiving one or more
matched data pairs, wherein the step of evaluating a predictive accuracy
comprises evaluating a correlation of at least one matched data pair with
at least some of the calibration information.

[0174] In an embodiment of the nineteenth aspect, the step of determining
is based at least in part on the correlation of the at least one matched
data pair and the calibration information.

[0175] In an embodiment of the nineteenth aspect, the step of receiving
and processing calibration information comprises receiving reference data
from a reference analyte monitor, forming at least one matched data pair,
forming a calibration set including said at least one matched data pair,
and forming a calibration line from said calibration set, wherein the
step of evaluating a predictive accuracy comprises evaluating a
correlation of the matched data pairs in the calibration set with the
calibration line.

[0176] In an embodiment of the nineteenth aspect, the step of determining
is based at least in part on the correlation of the matched pairs in the
calibration set and the calibration line.

[0177] In an embodiment of the nineteenth aspect, the step of receiving
and processing calibration information comprises receiving a matched data
pair and forming a calibration set including said matched data pair,
wherein the step of evaluating a predictive accuracy comprises evaluating
a discordance of the matched data pair and/or the matched data pairs in
the calibration set.

[0178] In an embodiment of the nineteenth aspect, the step of determining
is based at least in part on the discordance of the matched data pair
and/or the matched data pairs in the calibration set.

[0179] In an embodiment of the nineteenth aspect, the step of receiving
and processing calibration information comprises forming one or more
matched data pairs by matching time corresponding sensor and reference
data and forming a calibration set including one or more matched data
pairs wherein the step of evaluating a predictive accuracy comprises
iteratively evaluating a plurality of combinations of matched data pairs
in the calibration set to obtain a plurality of calibration lines.

[0180] In an embodiment of the nineteenth aspect, the method further
comprises removing matched data pairs from the calibration set in
response to the iterative evaluation.

[0181] In an embodiment of the nineteenth aspect, the step of determining
is based at least in part on the iterative evaluation.

[0182] In an embodiment of the nineteenth aspect, the step of receiving
and processing calibration information comprises receiving reference
data, forming one or more matched data pairs, and forming a calibration
set including one or more matched data pairs, wherein the step of
evaluating a predictive accuracy comprises evaluating an goodness of fit
of the calibration set with a calibration line drawn from the calibration
set.

[0183] In an embodiment of the nineteenth aspect, the step of determining
is based at least in part on the goodness of fit.

[0184] In an embodiment of the nineteenth aspect, the step of receiving
and processing calibration information comprises receiving reference
data, and wherein the step of evaluating a predictive accuracy comprises
evaluating a leverage of the reference data based at least in part on a
glucose concentration associated with the reference data.

[0185] In an embodiment of the nineteenth aspect, the step of determining
is based at least in part on the leverage of the reference data.

[0186] In an embodiment of the nineteenth aspect, the method further
comprises requesting reference data responsive to the step of
determining.

[0187] In an embodiment of the nineteenth aspect, the method further
comprises displaying an amount of time before reference data will be
requested.

[0188] In a twentieth aspect, a system is provided for calibration of a
continuous analyte sensor, comprising: a continuous analyte sensor
configured to continuously measure a concentration of analyte in a host;
and a computer system that receives sensor data from the continuous
analyte sensor, wherein the computer system is configured to receive and
process calibration information, and wherein the computer system
evaluates a predictive accuracy of calibration information to determine
when to request additional reference data.

[0189] In an embodiment of the twentieth aspect, the computer system
determines a time period to request additional reference data.

[0190] In an embodiment of the twentieth aspect, the time period is
between about 0 minutes and 7 days.

[0191] In an embodiment of the twentieth aspect, the computer system is
configured to receive reference data from a reference analyte monitor,
wherein the computer system is configured to match reference data to
substantially time corresponding sensor data to form at least one matched
data pair, and wherein the computer system is configured to evaluate a
predictive accuracy by evaluating a correlation of at least one matched
data pair with at least some of the calibration information.

[0192] In an embodiment of the twentieth aspect, the computer system
determines when to request reference data based at least in part on the
correlation of the at least one matched data pair and the calibration
information.

[0193] In an embodiment of the twentieth aspect, the computer system is
configured to receive reference data from a reference analyte monitor,
match reference data to substantially time corresponding sensor data to
form at least one matched data pair, form a calibration set from at least
one matched data pair, and form a calibration line from the calibration
set, wherein the computer system is configured to evaluate a predictive
accuracy by evaluating a correlation of matched data pairs in the
calibration set with a calibration line based on a calibration set
including a newly received matched data pair.

[0194] In an embodiment of the twentieth aspect, the computer system
determines when to request reference data based at least in part on the
correlation of the matched pairs in the calibration set and the
calibration line.

[0195] In an embodiment of the twentieth aspect, the computer system is
configured to receive reference data from a reference analyte monitor,
match reference data to substantially time corresponding sensor data to
form at least one matched data pair, and form a calibration set from at
least one matched data pair, wherein the computer system is configured to
evaluate a predictive accuracy by evaluating a discordance of a matched
data pair and/or a plurality of matched data pairs in a calibration set.

[0196] In an embodiment of the twentieth aspect, the computer system
determines when to request reference data based at least in part on the
discordance of the matched data pair and/or the matched data pairs in the
calibration set.

[0197] In an embodiment of the twentieth aspect, the computer system is
configured to receive reference data from a reference analyte monitor,
match reference data to substantially time corresponding sensor data to
form at least one matched data pair, and form a calibration set from at
least one matched data pair, wherein the computer system iteratively
evaluates a plurality of combinations of matched data pairs in the
calibration set to obtain a plurality of calibration lines.

[0198] In an embodiment of the twentieth aspect, the computer system is
configured to remove matched data pairs from the calibration set in
response to the iterative evaluation.

[0199] In an embodiment of the twentieth aspect, the computer system
determines when to request reference data based at least in part on the
iterative evaluation.

[0200] In an embodiment of the twentieth aspect, the computer system is
configured to receive reference data from a reference analyte monitor,
match reference data to substantially time corresponding sensor data to
form at least one matched data pair, and form a calibration set from at
least one matched data pair, wherein the computer system is configured to
evaluate a predictive accuracy by evaluating a goodness of fit of the
calibration set with a calibration line drawn from the calibration set.

[0201] In an embodiment of the twentieth aspect, the computer system
determines when to request reference data based at least in part on the
goodness of fit.

[0202] In an embodiment of the twentieth aspect, the computer system is
configured to receive reference data from a reference analyte monitor,
and wherein the computer system is configured to evaluate a predictive
accuracy by evaluating a leverage of the reference data based at least in
part on a glucose concentration associated with reference data.

[0203] In an embodiment of the twentieth aspect, the computer system
determines when to request reference data based at least in part on
leverage of the reference data.

[0204] In an embodiment of the twentieth aspect, the computer system is
configured to request reference data at a time determined by the
predictive evaluation.

[0205] In an embodiment of the twentieth aspect, the computer system is
configured to display an amount of time before reference data will be
requested.

[0206] In a twenty-first aspect, a method is provided for calibration of a
continuous glucose sensor, the method comprising: receiving a signal from
a continuous glucose sensor, evaluating a sensor performance during a
sensor session; and determining when to request reference data responsive
to the sensor performance determined.

[0207] In an embodiment of the twenty-first aspect, the step of evaluating
a sensor performance comprises determining an amount of drift on the
sensor signal over a time period, and wherein the step of determining
when to request reference data comprises requesting reference data when
the amount of drift is greater than a threshold.

[0208] In an embodiment of the twenty-first aspect, the step of
determining an amount of drift comprises monitoring a change in signal
strength.

[0209] In an embodiment of the twenty-first aspect, the step of
determining an amount of drift comprises analyzing a fluctuation in a
second working electrode of a dual electrode system.

[0210] In an embodiment of the twenty-first aspect, the step of monitoring
a change in signal strength comprises applying a low pass filter.

[0211] In an embodiment of the twenty-first aspect, the step of
determining an amount of drift comprises monitoring a change in
calibration information.

[0212] In an embodiment of the twenty-first aspect, the method further
comprises controlling an output in response to the sensor performance.

[0213] In a twenty-second aspect, a method is provided for calibration of
a continuous glucose sensor, the method comprising: receiving a signal
from a continuous glucose sensor; determining a predictive accuracy of
sensor calibration; and controlling an output based at least in part on
the predictive accuracy determined.

[0214] In an embodiment of the twenty-second aspect, the step of
controlling an output comprises controlling a display of data based at
least in part on the level of noise.

[0215] In an embodiment of the twenty-second aspect, the step of
controlling an output comprises controlling alarms indicative of at least
one of hypoglycemia, hyperglycemia, predicted hypoglycemia, and predicted
hyperglycemia based at least in part on the predictive accuracy.

[0216] In an embodiment of the twenty-second aspect, the method further
comprises controlling insulin delivery and/or insulin therapy
instructions based at least in part on the predictive accuracy.

[0217] In an embodiment of the twenty-second aspect, the method further
comprises diagnosing a sensor condition based at least in part on the
predictive accuracy.

BRIEF DESCRIPTION OF THE DRAWINGS

[0218] FIG. 1A is an exploded perspective view of a glucose sensor in one
embodiment.

[0219] FIG. 1B is side view of a distal portion of a transcutaneously
inserted sensor in one embodiment.

[0220] FIG. 1C is a cross-sectional schematic view of a sensing region of
a dual-electrode continuous analyte sensor in one embodiment wherein an
active enzyme of an enzyme domain is positioned over the first working
electrode but not over the second working electrode.

[0221]FIG. 2 is a block diagram that illustrates sensor electronics in
one embodiment.

[0222] FIGS. 3A to 3D are schematic views of a receiver in first, second,
third, and fourth embodiments, respectively.

[0223]FIG. 4A is a block diagram of receiver electronics in one
embodiment.

[0224] FIG. 4B is an illustration of the receiver in one embodiment
showing an analyte trend graph, including measured analyte values,
estimated analyte values, and a clinical risk zone.

[0225]FIG. 4c is an illustration of the receiver in another embodiment
showing a representation of analyte concentration and directional trend
using a gradient bar.

[0226]FIG. 4D is an illustration of the receiver in yet another
embodiment, including a screen that shows a numerical representation of
the most recent measured analyte value.

[0227]FIG. 5 is a block diagram of an integrated system of the preferred
embodiments, including a continuous glucose sensor, a receiver for
processing and displaying sensor data, a medicament delivery device, and
an optional single point glucose-monitoring device.

[0228]FIG. 6A is a flow chart that illustrates the process of calibrating
the sensor data in one embodiment.

[0229]FIG. 6B is a graph that illustrates a linear regression used to
calibrate the sensor data in one embodiment.

[0230]FIG. 6c is a flow chart that illustrates the process of immediate
calibration of a continuous analyte sensor in one embodiment.

[0231]FIG. 7 is a flow chart that illustrates the process of smart or
intelligent calibration of a continuous analyte sensor in one embodiment.

[0232]FIG. 8A is a graph illustrating the components of a signal measured
by a transcutaneous glucose sensor (after sensor break-in was complete),
implanted in a non-diabetic, human volunteer host.

[0233]FIG. 8B is a graph that shows a raw data stream obtained from a
glucose sensor over a 4 hour time span in one example.

[0234]FIG. 8c is a graph that shows a raw data stream obtained from a
glucose sensor over a 36 hour time span in another example.

[0235]FIG. 9 is a flow chart that illustrates the process of detecting
and replacing transient non-glucose related signal artifacts in a data
stream in one embodiment.

[0236] FIG. 10 is a graph that illustrates a method of classifying noise
in a data stream from a glucose sensor in one embodiment.

[0237]FIG. 11 is a flow chart that illustrates a method of detecting and
processing signal artifacts in the data stream from a glucose sensor in
one embodiment.

[0238]FIG. 12 is a graph that illustrates a raw data stream from a
glucose sensor for approximately 24 hours with a filtered version of the
same data stream superimposed on the same graph.

[0239]FIG. 13 is a flow chart that illustrates a method of calculating a
rate of change of sensor data from a glucose sensor in one embodiment.

[0240]FIG. 14 is a flow chart that illustrates a method of predicting
hypoglycemic and/or hyperglycemic episodes based on continuous glucose
sensor data in one embodiment.

[0241]FIG. 15 is a flow chart that illustrates a method of setting a mode
and further processing data based upon a mode setting in one embodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0242] The following description and examples illustrate some exemplary
embodiments of the disclosed invention in detail. Those of skill in the
art will recognize that there are numerous variations and modifications
of this invention that are encompassed by its scope. Accordingly, the
description of a certain exemplary embodiment should not be deemed to
limit the scope of the present invention.

DEFINITIONS

[0243] In order to facilitate an understanding of the preferred
embodiments, a number of terms are defined below.

[0245] The term "ROM" as used herein is a broad term and is to be given
its ordinary and customary meaning to a person of ordinary skill in the
art (and is not to be limited to a special or customized meaning), and
furthermore refers without limitation to read-only memory, which is a
type of data storage device manufactured with fixed contents. ROM is
broad enough to include EEPROM, for example, which is electrically
erasable programmable read-only memory (ROM).

[0246] The term "RAM" as used herein is a broad term and is to be given
its ordinary and customary meaning to a person of ordinary skill in the
art (and is not to be limited to a special or customized meaning), and
furthermore refers without limitation to a data storage device for which
the order of access to different locations does not affect the speed of
access. RAM is broad enough to include RAM, for example, which is static
random access memory that retains data bits in its memory as long as
power is being supplied.

[0247] The term "A/D Converter" as used herein is a broad term and is to
be given its ordinary and customary meaning to a person of ordinary skill
in the art (and is not to be limited to a special or customized meaning),
and furthermore refers without limitation to hardware and/or software
that converts analog electrical signals into corresponding digital
signals.

[0248] The terms "processor module," "microprocessor" and "processor" as
used herein are broad terms and are to be given their ordinary and
customary meaning to a person of ordinary skill in the art (and are not
to be limited to a special or customized meaning), and furthermore refer
without limitation to a computer system, state machine, and the like that
performs arithmetic and logic operations using logic circuitry that
responds to and processes the basic instructions that drive a computer.

[0249] The term "RF transceiver" as used herein is a broad term and is to
be given its ordinary and customary meaning to a person of ordinary skill
in the art (and is not to be limited to a special or customized meaning),
and furthermore refers without limitation to a radio frequency
transmitter and/or receiver for transmitting and/or receiving signals.

[0250] The term "jitter" as used herein is a broad term and is to be given
its ordinary and customary meaning to a person of ordinary skill in the
art (and is not to be limited to a special or customized meaning), and
furthermore refers without limitation to noise above and below the mean
caused by ubiquitous noise caused by a circuit and/or environmental
effects; jitter can be seen in amplitude, phase timing, or the width of
the signal pulse.

[0251] The terms "raw data stream" and "data stream" as used herein are
broad terms and are to be given their ordinary and customary meaning to a
person of ordinary skill in the art (and are not to be limited to a
special or customized meaning), and furthermore refer without limitation
to an analog or digital signal directly related to the measured glucose
from the glucose sensor. In one example, the raw data stream is digital
data in "counts" converted by an A/D converter from an analog signal
(e.g., voltage or amps) and includes one or more data points
representative of a glucose concentration. The terms broadly encompass a
plurality of time spaced data points from a substantially continuous
glucose sensor, which comprises individual measurements taken at time
intervals ranging from fractions of a second up to, e.g., 1, 2, or 5
minutes or longer. In another example, the raw data stream includes an
integrated digital value, wherein the data includes one or more data
points representative of the glucose sensor signal averaged over a time
period.

[0252] The term "calibration" as used herein is a broad term and is to be
given its ordinary and customary meaning to a person of ordinary skill in
the art (and is not to be limited to a special or customized meaning),
and furthermore refers without limitation to the process of determining
the relationship between the sensor data and the corresponding reference
data, which can be used to convert sensor data into meaningful values
substantially equivalent to the reference data. In some embodiments,
namely, in continuous analyte sensors, calibration can be updated or
recalibrated over time as changes in the relationship between the sensor
data and reference data occur, for example, due to changes in
sensitivity, baseline, transport, metabolism, and the like.

[0253] The terms "calibrated data" and "calibrated data stream" as used
herein are broad terms and are to be given their ordinary and customary
meaning to a person of ordinary skill in the art (and are not to be
limited to a special or customized meaning), and furthermore refer
without limitation to data that has been transformed from its raw state
to another state using a function, for example a conversion function, to
provide a meaningful value to a user.

[0254] The terms "smoothed data" and "filtered data" as used herein are
broad terms and are to be given their ordinary and customary meaning to a
person of ordinary skill in the art (and are not to be limited to a
special or customized meaning), and furthermore refer without limitation
to data that has been modified to make it smoother and more continuous
and/or to remove or diminish outlying points, for example, by performing
a moving average of the raw data stream. Examples of data filters include
FIR (finite impulse response), IIR (infinite impulse response), moving
average filters, and the like.

[0255] The terms "smoothing" and "filtering" as used herein are broad
terms and are to be given their ordinary and customary meaning to a
person of ordinary skill in the art (and are not to be limited to a
special or customized meaning), and furthermore refer without limitation
to a mathematical computation that attenuates components of a signal that
are undesired, such as reducing noise errors in a signal. In some
embodiments, smoothing refers to modification of a set of data to make it
smoother and more continuous or to remove or diminish outlying points,
for example, by performing a moving average of the raw data stream.

[0256] The term "noise signal" as used herein is a broad term and is to be
given its ordinary and customary meaning to a person of ordinary skill in
the art (and is not to be limited to a special or customized meaning),
and furthermore refers without limitation to a signal associated with
noise on the data stream (e.g., non-analyte related signal). The noise
signal can be determined by filtering and/or averaging, for example. In
some embodiments, the noise signal is a signal residual, delta residual
(difference of residual), absolute delta residual, and/or the like, which
are described in more detail elsewhere herein.

[0257] The term "algorithm" as used herein is a broad term and is to be
given its ordinary and customary meaning to a person of ordinary skill in
the art (and is not to be limited to a special or customized meaning),
and furthermore refers without limitation to a computational process (for
example, programs) involved in transforming information from one state to
another, for example, by using computer processing.

[0258] The term "matched data pairs" as used herein is a broad term and is
to be given its ordinary and customary meaning to a person of ordinary
skill in the art (and is not to be limited to a special or customized
meaning), and furthermore refers without limitation to reference data
(for example, one or more reference analyte data points) matched with
substantially time corresponding sensor data (for example, one or more
sensor data points).

[0259] The term "counts" as used herein is a broad term and is to be given
its ordinary and customary meaning to a person of ordinary skill in the
art (and is not to be limited to a special or customized meaning), and
furthermore refers without limitation to a unit of measurement of a
digital signal. In one example, a raw data stream measured in counts is
directly related to a voltage (e.g., converted by an A/D converter),
which is directly related to current from the working electrode. In
another example, counter electrode voltage measured in counts is directly
related to a voltage.

[0260] The term "sensor" as used herein is a broad term and is to be given
its ordinary and customary meaning to a person of ordinary skill in the
art (and is not to be limited to a special or customized meaning), and
furthermore refers without limitation to the component or region of a
device by which an analyte can be quantified and/or the device itself.

[0261] The terms "glucose sensor" and "member for determining the amount
of glucose in a biological sample" as used herein are broad terms and are
to be given their ordinary and customary meaning to a person of ordinary
skill in the art (and are not to be limited to a special or customized
meaning), and furthermore refer without limitation to any mechanism
(e.g., enzymatic or non-enzymatic) by which glucose can be quantified.
For example, some embodiments utilize a membrane that contains glucose
oxidase that catalyzes the conversion of oxygen and glucose to hydrogen
peroxide and gluconate, as illustrated by the following chemical
reaction:

Glucose+O2→Gluconate+H2O2

[0262] Because for each glucose molecule metabolized, there is a
proportional change in the co-reactant O2 and the product
H2O2, one can use an electrode to monitor the current change in
either the co-reactant or the product to determine glucose concentration.

[0263] The terms "operably connected" and "operably linked" as used herein
are broad terms and are to be given their ordinary and customary meaning
to a person of ordinary skill in the art (and are not to be limited to a
special or customized meaning), and furthermore refer without limitation
to one or more components being linked to another component(s) in a
manner that allows transmission of signals between the components. For
example, one or more electrodes can be used to detect the amount of
glucose in a sample and convert that information into a signal, e.g., an
electrical or electromagnetic signal; the signal can then be transmitted
to an electronic circuit. In this case, the electrode is "operably
linked" to the electronic circuitry. These terms are broad enough to
include wireless connectivity.

[0264] The term "electronic circuitry" as used herein is a broad term and
is to be given its ordinary and customary meaning to a person of ordinary
skill in the art (and is not to be limited to a special or customized
meaning), and furthermore refers without limitation to the components of
a device configured to process biological information obtained from a
host. In the case of a glucose-measuring device, the biological
information is obtained by a sensor regarding a particular glucose in a
biological fluid, thereby providing data regarding the amount of that
glucose in the fluid. U.S. Pat. Nos. 4,757,022, 5,497,772 and 4,787,398,
which are hereby incorporated by reference, describe suitable electronic
circuits that can be utilized with devices including the biointerface
membrane of a preferred embodiment.

[0265] The term "substantially" as used herein is a broad term and is to
be given its ordinary and customary meaning to a person of ordinary skill
in the art (and is not to be limited to a special or customized meaning),
and furthermore refers without limitation to being largely but not
necessarily wholly that which is specified.

[0266] The term "host" as used herein is a broad term and is to be given
its ordinary and customary meaning to a person of ordinary skill in the
art (and is not to be limited to a special or customized meaning), and
furthermore refers without limitation to mammals, particularly humans.

[0267] The term "continuous analyte (or glucose) sensor" as used herein is
a broad term and is to be given its ordinary and customary meaning to a
person of ordinary skill in the art (and is not to be limited to a
special or customized meaning), and furthermore refers without limitation
to a device that continuously or continually measures a concentration of
an analyte, for example, at time intervals ranging from fractions of a
second up to, for example, 1, 2, or 5 minutes, or longer. In one
exemplary embodiment, the continuous analyte sensor is a glucose sensor
such as described in U.S. Pat. No. 6,001,067, which is incorporated
herein by reference in its entirety.

[0268] The term "continuous analyte (or glucose) sensing" as used herein
is a broad term and is to be given its ordinary and customary meaning to
a person of ordinary skill in the art (and is not to be limited to a
special or customized meaning), and furthermore refers without limitation
to the period in which monitoring of an analyte is continuously or
continually performed, for example, at time intervals ranging from
fractions of a second up to, for example, 1, 2, or 5 minutes, or longer.

[0269] The terms "reference analyte monitor," "reference analyte meter,"
and "reference analyte sensor" as used herein are broad terms and are to
be given their ordinary and customary meaning to a person of ordinary
skill in the art (and are not to be limited to a special or customized
meaning), and furthermore refer without limitation to a device that
measures a concentration of an analyte and can be used as a reference for
the continuous analyte sensor, for example a self-monitoring blood
glucose meter (SMBG) can be used as a reference for a continuous glucose
sensor for comparison, calibration, and the like.

[0270] The term "Clarke Error Grid", as used herein, is a broad term and
is to be given its ordinary and customary meaning to a person of ordinary
skill in the art (and is not to be limited to a special or customized
meaning), and refers without limitation to an error grid analysis, which
evaluates the clinical significance of the difference between a reference
glucose value and a sensor generated glucose value, taking into account
1) the value of the reference glucose measurement, 2) the value of the
sensor glucose measurement, 3) the relative difference between the two
values, and 4) the clinical significance of this difference. See Clarke
et al., "Evaluating Clinical Accuracy of Systems for Self-Monitoring of
Blood Glucose", Diabetes Care, Volume 10, Number 5, September-October
1987, which is incorporated by reference herein in its entirety.

[0271] The term "Consensus Error Grid", as used herein, is a broad term
and is to be given its ordinary and customary meaning to a person of
ordinary skill in the art (and is not to be limited to a special or
customized meaning), and refers without limitation to an error grid
analysis that assigns a specific level of clinical risk to any possible
error between two time corresponding glucose measurements. The Consensus
Error Grid is divided into zones signifying the degree of risk posed by
the deviation. See Parkes et al., "A New Consensus Error Grid to Evaluate
the Clinical Significance of Inaccuracies in the Measurement of Blood
Glucose", Diabetes Care, Volume 23, Number 8, August 2000, which is
incorporated by reference herein in its entirety.

[0272] The term "clinical acceptability", as used herein, is a broad term
and is to be given its ordinary and customary meaning to a person of
ordinary skill in the art (and is not to be limited to a special or
customized meaning), and refers without limitation to determination of
the risk of inaccuracies to a patient. Clinical acceptability considers a
deviation between time corresponding glucose measurements (e.g., data
from a glucose sensor and data from a reference glucose monitor) and the
risk (e.g., to the decision making of a diabetic patient) associated with
that deviation based on the glucose value indicated by the sensor and/or
reference data. One example of clinical acceptability may be 85% of a
given set of measured analyte values within the "A" and "B" region of a
standard Clarke Error Grid when the sensor measurements are compared to a
standard reference measurement.

[0273] The term "R-value," as used herein is a broad term and is to be
given its ordinary and customary meaning to a person of ordinary skill in
the art (and is not to be limited to a special or customized meaning),
and refers without limitation to one conventional way of summarizing the
correlation of data; that is, a statement of what residuals (e.g., root
mean square deviations) are to be expected if the data are fitted to a
straight line by the a regression.

[0274] The terms "data association" and "data association function," as
used herein, are broad terms and are to be given their ordinary and
customary meaning to a person of ordinary skill in the art (and are not
to be limited to a special or customized meaning), and refer without
limitation to a statistical analysis of data and particularly its
correlation to, or deviation from, from a particular curve. A data
association function is used to show data association. For example, data
that form as calibration set as described herein may be analyzed
mathematically to determine its correlation to, or deviation from, a
curve (e.g., line or set of lines) that defines the conversion function;
this correlation or deviation is the data association. A data association
function is used to determine data association. Examples of data
association functions include, but are not limited to, linear regression,
non-linear mapping/regression, rank (e.g., non-parametric) correlation,
least mean square fit, mean absolute deviation (MAD), mean absolute
relative difference. In one such example, the correlation coefficient of
linear regression is indicative of the amount of data association of the
calibration set that forms the conversion function, and thus the quality
of the calibration.

[0275] The term "quality of calibration" as used herein, is a broad term
and is to be given its ordinary and customary meaning to a person of
ordinary skill in the art (and is not to be limited to a special or
customized meaning), and refers without limitation to the statistical
association of matched data pairs in the calibration set used to create
the conversion function. For example, an R-value may be calculated for a
calibration set to determine its statistical data association, wherein an
R-value greater than 0.79 determines a statistically acceptable
calibration quality, while an R-value less than 0.79 determines
statistically unacceptable calibration quality.

[0276] The terms "congruence" and "correlation" as used herein, are broad
terms and are to be given their ordinary and customary meaning to a
person of ordinary skill in the art (and are not to be limited to a
special or customized meaning), and refer without limitation to the
quality or state of agreeing, coinciding, or being concordant. In one
example, correlation may be determined using a data association function.

[0277] The term "discordance" as used herein, is a broad term and is to be
given its ordinary and customary meaning to a person of ordinary skill in
the art (and is not to be limited to a special or customized meaning),
and refers without limitation to the disassociation of comparative data.
In one example, discordance may be determined using a data association
function.

[0278] The phrase "goodness of fit" as used herein, is a broad term and is
to be given its ordinary and customary meaning to a person of ordinary
skill in the art (and is not to be limited to a special or customized
meaning), and refers without limitation to a degree to which a model fits
the observed data. For example, in a regression analysis, the
goodness-of-fit can be quantified in terms of R-squared, R-value and/or
error distribution.

[0279] The term "sensor session" as used herein, is a broad term and is to
be given its ordinary and customary meaning to a person of ordinary skill
in the art (and is not to be limited to a special or customized meaning),
and refers without limitation to a period of time a sensor is in use,
such as but not limited to a period of time starting at the time the
sensor is implanted (e.g., by the host) to removal of the sensor (e.g.,
removal of the sensor from the host's body and/or removal of the
transmitter from the sensor housing).

[0280] The terms "sensor head" and "sensing region" as used herein are
broad terms and are to be given their ordinary and customary meaning to a
person of ordinary skill in the art (and are not to be limited to a
special or customized meaning), and furthermore refer without limitation
to the region of a monitoring device responsible for the detection of a
particular analyte. The sensing region generally comprises a
non-conductive body, a working electrode (anode), a reference electrode
(optional), and/or a counter electrode (cathode) passing through and
secured within the body forming electrochemically reactive surfaces on
the body and an electronic connective means at another location on the
body, and a multi-domain membrane affixed to the body and covering the
electrochemically reactive surface.

[0281] The term "physiologically feasible" as used herein is a broad term
and is to be given its ordinary and customary meaning to a person of
ordinary skill in the art (and is not to be limited to a special or
customized meaning), and furthermore refers without limitation to the
physiological parameters obtained from continuous studies of glucose data
in humans and/or animals. For example, a maximal sustained rate of change
of glucose in humans of about 4 to 5 mg/dL/min and a maximum acceleration
of the rate of change of about 0.1 to 0.2 mg/dL/min/min are deemed
physiologically feasible limits. Values outside of these limits would be
considered non-physiological and likely a result of signal error, for
example. As another example, the rate of change of glucose is lowest at
the maxima and minima of the daily glucose range, which are the areas of
greatest risk in patient treatment, thus a physiologically feasible rate
of change can be set at the maxima and minima based on continuous studies
of glucose data. As a further example, it has been observed that the best
solution for the shape of the curve at any point along glucose signal
data stream over a certain time period (e.g., about 20 to 30 minutes) is
a straight line, which can be used to set physiological limits.

[0282] The term "system noise" as used herein is a broad term and is to be
given its ordinary and customary meaning to a person of ordinary skill in
the art (and is not to be limited to a special or customized meaning),
and furthermore refers without limitation to unwanted electronic or
diffusion-related noise which can include Gaussian, motion-related,
flicker, kinetic, or other white noise, for example.

[0283] The terms "noise," "noise event(s)," "noise episode(s)," "signal
artifact(s)," "signal artifact event(s)," and "signal artifact
episode(s)" as used herein are broad terms and are to be given their
ordinary and customary meaning to a person of ordinary skill in the art
(and are not to be limited to a special or customized meaning), and
furthermore refer without limitation to signal noise that is caused by
substantially non-glucose related, such as interfering species, macro- or
micro-motion, ischemia, pH changes, temperature changes, pressure,
stress, or even unknown sources of mechanical, electrical and/or
biochemical noise for example. In some embodiments, signal artifacts are
transient and characterized by a higher amplitude than system noise, and
described as "transient non-glucose related signal artifact(s) that have
a higher amplitude than system noise." In some embodiments, noise is
caused by rate-limiting (or rate-increasing) phenomena. In some
circumstances, the source of the noise is unknown.

[0284] The terms "constant noise" and "constant background" as used herein
are broad terms, and are to be given their ordinary and customary meaning
to a person of ordinary skill in the art (and are not to be limited to a
special or customized meaning), and refer without limitation to the
component of the noise signal that remains relatively constant over time.
In some embodiments, constant noise may be referred to as "background" or
"baseline." For example, certain electroactive compounds found in the
human body are relatively constant factors (e.g., baseline of the host's
physiology). In some circumstances, constant background noise can slowly
drift over time (e.g., increase or decrease), however this drift need not
adversely affect the accuracy of a sensor, for example, because a sensor
can be calibrated and re-calibrated and/or the drift measured and
compensated for.

[0285] The terms "non-constant noise," "non-constant background," "noise
event(s)," "noise episode(s)," "signal artifact(s)," "signal artifact
event(s)," and "signal artifact episode(s)" as used herein are broad
terms, and are to be given their ordinary and customary meaning to a
person of ordinary skill in the art (and are not to be limited to a
special or customized meaning), and refer without limitation to a
component of the background signal (e.g., non-analyte related signal)
that is relatively non-constant, for example, transient and/or
intermittent. For example, certain electroactive compounds, are
relatively non-constant due to the host's ingestion, metabolism, wound
healing, and other mechanical, chemical and/or biochemical factors),
which create intermittent (e.g., non-constant) "noise" on the sensor
signal that can be difficult to "calibrate out" using a standard
calibration equations (e.g., because the background of the signal does
not remain constant).

[0286] The term "linear regression" as used herein is a broad term and is
to be given its ordinary and customary meaning to a person of ordinary
skill in the art (and is not to be limited to a special or customized
meaning), and furthermore refers without limitation to finding a line in
which a set of data has a minimal measurement from that line. Byproducts
of this algorithm include a slope, a y-intercept, and an R-Squared value
that determine how well the measurement data fits the line.

[0287] The term "non-linear regression" as used herein is a broad term and
is to be given its ordinary and customary meaning to a person of ordinary
skill in the art (and is not to be limited to a special or customized
meaning), and furthermore refers without limitation to fitting a set of
data to describe the relationship between a response variable and one or
more explanatory variables in a non-linear fashion.

[0288] The term "mean" as used herein is a broad term and is to be given
its ordinary and customary meaning to a person of ordinary skill in the
art (and is not to be limited to a special or customized meaning), and
furthermore refers without limitation to the sum of the observations
divided by the number of observations.

[0289] The term "trimmed mean" as used herein is a broad term and is to be
given its ordinary and customary meaning to a person of ordinary skill in
the art (and is not to be limited to a special or customized meaning),
and furthermore refers without limitation to a mean taken after extreme
values in the tails of a variable (e.g., highs and lows) are eliminated
or reduced (e.g., "trimmed"). The trimmed mean compensates for
sensitivities to extreme values by dropping a certain percentage of
values on the tails. For example, the 50% trimmed mean is the mean of the
values between the upper and lower quartiles. The 90% trimmed mean is the
mean of the values after truncating the lowest and highest 5% of the
values. In one example, two highest and two lowest measurements are
removed from a data set and then the remaining measurements are averaged.

[0290] The term "non-recursive filter" as used herein is a broad term and
is to be given its ordinary and customary meaning to a person of ordinary
skill in the art (and is not to be limited to a special or customized
meaning), and furthermore refers without limitation to an equation that
uses moving averages as inputs and outputs.

[0291] The terms "recursive filter" and "auto-regressive algorithm" as
used herein are broad terms and are to be given their ordinary and
customary meaning to a person of ordinary skill in the art (and are not
to be limited to a special or customized meaning), and furthermore refer
without limitation to an equation in which includes previous averages are
part of the next filtered output. More particularly, the generation of a
series of observations whereby the value of each observation is partly
dependent on the values of those that have immediately preceded it. One
example is a regression structure in which lagged response values assume
the role of the independent variables.

[0292] The term "signal estimation algorithm factors" as used herein is a
broad term and is to be given its ordinary and customary meaning to a
person of ordinary skill in the art (and is not to be limited to a
special or customized meaning), and furthermore refers without limitation
to one or more algorithms that use historical and/or present signal data
stream values to estimate unknown signal data stream values. For example,
signal estimation algorithm factors can include one or more algorithms,
such as linear or non-linear regression. As another example, signal
estimation algorithm factors can include one or more sets of coefficients
that can be applied to one algorithm.

[0293] The terms "physiological parameters" and "physiological boundaries"
as used herein are broad terms and are to be given their ordinary and
customary meaning to a person of ordinary skill in the art (and are not
to be limited to a special or customized meaning), and furthermore refer
without limitation to the parameters obtained from continuous studies of
physiological data in humans and/or animals. For example, a maximal
sustained rate of change of glucose in humans of about 4 to 5 mg/dL/min
and a maximum acceleration of the rate of change of about 0.1 to 0.2
mg/dL/min2 are deemed physiologically feasible limits; values
outside of these limits would be considered non-physiological. As another
example, the rate of change of glucose is lowest at the maxima and minima
of the daily glucose range, which are the areas of greatest risk in
patient treatment, thus a physiologically feasible rate of change can be
set at the maxima and minima based on continuous studies of glucose data.
As a further example, it has been observed that the best solution for the
shape of the curve at any point along glucose signal data stream over a
certain time period (for example, about 20 to 30 minutes) is a straight
line, which can be used to set physiological limits. These terms are
broad enough to include physiological parameters for any analyte.

[0294] The term "measured analyte values" as used herein is a broad term
and is to be given its ordinary and customary meaning to a person of
ordinary skill in the art (and is not to be limited to a special or
customized meaning), and furthermore refers without limitation to an
analyte value or set of analyte values for a time period for which
analyte data has been measured by an analyte sensor. The term is broad
enough to include data from the analyte sensor before or after data
processing in the sensor and/or receiver (for example, data smoothing,
calibration, and the like).

[0295] The term "estimated analyte values" as used herein is a broad term
and is to be given its ordinary and customary meaning to a person of
ordinary skill in the art (and is not to be limited to a special or
customized meaning), and furthermore refers without limitation to an
analyte value or set of analyte values, which have been algorithmically
extrapolated from measured analyte values. In some embodiments, estimated
analyte values are estimated for a time period during which no data
exists. However, estimated analyte values can also be estimated during a
time period for which measured data exists, but is to be replaced by
algorithmically extrapolated (e.g. processed or filtered) data due to
noise or a time lag in the measured data, for example.

[0296] The term "calibration information" as used herein is a broad term
and is to be given its ordinary and customary meaning to a person of
ordinary skill in the art (and is not to be limited to a special or
customized meaning), and furthermore refers without limitation to any
information useful in calibration of a sensor. Calibration information
includes reference data received from a reference analyte monitor,
including one or more reference data points, one or more matched data
pairs formed by matching reference data (e.g., one or more reference
glucose data points) with substantially time corresponding sensor data
(e.g., one or more continuous sensor data points), a calibration set
formed from a set of one or more matched data pairs, and/or a calibration
line drawn from the calibration set, for example.

[0297] The term "mode" as used herein is a broad term and is to be given
its ordinary and customary meaning to a person of ordinary skill in the
art (and is not to be limited to a special or customized meaning), and
furthermore refers without limitation to an automatic and/or user
configurable setting within a system associated with an activity, event,
physiological condition, sensor condition, and/or preference of a user.

[0298] The term "mode profile" as used herein is a broad term and is to be
given its ordinary and customary meaning to a person of ordinary skill in
the art (and is not to be limited to a special or customized meaning),
and furthermore refers without limitation to a programmable,
predetermined, and/or user selectable sequence of modes based on time. In
one embodiment, the mode profile enables an automated setting of modes
based upon a mode profile, which can be associated with, for example, a
schedule of events or blocks of events corresponding to various times
throughout their day.

[0300] The preferred embodiments relate to the use of an analyte sensor
that measures a concentration of glucose or a substance indicative of the
concentration or presence of the analyte. In some embodiments, the
analyte sensor is a continuous device, for example a subcutaneous,
transdermal, or intravascular device. In some embodiments, the device can
analyze a plurality of intermittent blood samples. The analyte sensor can
use any method of glucose-measurement, including enzymatic, chemical,
physical, electrochemical, spectrophotometric, polarimetric,
calorimetric, iontophoretic, radiometric, and the like.

[0301] The analyte sensor can use any known method, including invasive,
minimally invasive, and non-invasive sensing techniques, to provide a
data stream indicative of the concentration of the analyte in a host. The
data stream is typically a raw data signal that is used to provide a
useful value of the analyte to a user, such as a patient or doctor, who
may be using the sensor.

Sensor

[0302] Although much of the description and examples are drawn to a
glucose sensor, the systems and methods of the preferred embodiments can
be applied to any measurable analyte. In some preferred embodiments, the
analyte sensor is a glucose sensor capable of measuring the concentration
of glucose in a host. One exemplary embodiment is described below, which
utilizes an implantable glucose sensor. However, it should be understood
that the devices and methods described herein can be applied to any
device capable of detecting a concentration of analyte and providing an
output signal that represents the concentration of the analyte.

[0303] In one preferred embodiment, the analyte sensor is an implantable
glucose sensor, such as described with reference to U.S. Pat. No.
6,001,067 and U.S. Patent Publication No. US-2005-0027463-A1. In another
preferred embodiment, the analyte sensor is a transcutaneous glucose
sensor, such as described with reference to U.S. Patent Publication No.
US-2006-0020187-A1. In yet another preferred embodiment, the analyte
sensor is a dual electrode analyte sensor, such as described with
reference to U.S. patent application Ser. No. 12/055,149. In still other
embodiments, the sensor is configured to be implanted in a host vessel or
extracorporeally, such as is described in U.S. Patent Publication No.
US-2007-0027385-A1, co-pending U.S. patent application Ser. No.
11/543,396 filed Oct. 4, 2006, co-pending U.S. patent application Ser.
No. 11/691,426 filed on Mar. 26, 2007, and co-pending U.S. patent
application Ser. No. 11/675,063 filed on Feb. 14, 2007. In one
alternative embodiment, the continuous glucose sensor comprises a
transcutaneous sensor such as described in U.S. Pat. No. 6,565,509 to Say
et al., for example. In another alternative embodiment, the continuous
glucose sensor comprises a subcutaneous sensor such as described with
reference to U.S. Pat. No. 6,579,690 to Bonnecaze et al. or U.S. Pat. No.
6,484,046 to Say et al., for example. In another alternative embodiment,
the continuous glucose sensor comprises a refillable subcutaneous sensor
such as described with reference to U.S. Pat. No. 6,512,939 to Colvin et
al., for example. In another alternative embodiment, the continuous
glucose sensor comprises an intravascular sensor such as described with
reference to U.S. Pat. No. 6,477,395 to Schulman et al., for example. In
another alternative embodiment, the continuous glucose sensor comprises
an intravascular sensor such as described with reference to U.S. Pat. No.
6,424,847 to Mastrototaro et al.

[0304] FIG. 1A is an exploded perspective view of one exemplary embodiment
comprising an implantable glucose sensor 100A that utilizes amperometric
electrochemical sensor technology to measure glucose concentration. In
this exemplary embodiment, a body 110 and head 112 house the electrodes
114 and sensor electronics, which are described in more detail below with
reference to FIG. 2. Three electrodes 114 are operably connected to the
sensor electronics (FIG. 2) and are covered by a sensing membrane 116 and
a biointerface membrane 118, which are attached by a clip 119.

[0305] In one embodiment, the three electrodes 114, which protrude through
the head 112, include a platinum working electrode, a platinum counter
electrode, and a silver/silver chloride reference electrode. The top ends
of the electrodes are in contact with an electrolyte phase (not shown),
which is a free-flowing fluid phase disposed between the sensing membrane
116 and the electrodes 114. The sensing membrane 116 includes an enzyme,
e.g., glucose oxidase, which covers the electrolyte phase. The
biointerface membrane 118 covers the sensing membrane 116 and serves, at
least in part, to protect the sensor 100A from external forces that can
result in environmental stress cracking of the sensing membrane 116.

[0306] In the illustrated embodiment, the counter electrode is provided to
balance the current generated by the species being measured at the
working electrode. In the case of a glucose oxidase based glucose sensor,
the species being measured at the working electrode is H2O2.
Glucose oxidase catalyzes the conversion of oxygen and glucose to
hydrogen peroxide and gluconate according to the following reaction:

Glucose+O2→Gluconate+H2O2

[0307] The change in H2O2 can be monitored to determine glucose
concentration because for each glucose molecule metabolized, there is a
proportional change in the product H2O2. Oxidation of
H2O2 by the working electrode is balanced by reduction of
ambient oxygen, enzyme generated H2O2, or other reducible
species at the counter electrode. The H2O2 produced from the
glucose oxidase reaction further reacts at the surface of working
electrode and produces two protons (2H.sup.+), two electrons (2e.sup.-),
and one oxygen molecule (O2).

[0308] FIG. 1B is side view of a distal portion 120 of a transcutaneously-
or intravascularly-inserted sensor 100B in one embodiment, showing
working and reference electrodes. In preferred embodiments, the sensor
100B is formed from a working electrode 122 and a reference electrode 124
helically wound around the working electrode 122. An insulator 126 is
disposed between the working and reference electrodes to provide
necessary electrical insulation there between. Certain portions of the
electrodes are exposed to enable electrochemical reaction thereon, for
example, a window 128 can be formed in the insulator to expose a portion
of the working electrode 122 for electrochemical reaction.

[0309] In preferred embodiments, each electrode is formed from a fine wire
with a diameter of from about 0.001 or less to about 0.010 inches or
more, for example, and is formed from, e.g., a plated insulator, a plated
wire, or bulk electrically conductive material. Although the illustrated
electrode configuration and associated text describe one preferred method
of forming a sensor, a variety of known sensor configurations can be
employed with the analyte sensor system of the preferred embodiments,
such as are described in U.S. Pat. No. 6,695,860 to Ward et al., U.S.
Pat. No. 6,565,509 to Say et al., U.S. Pat. No. 6,248,067 to Causey III,
et al., and U.S. Pat. No. 6,514,718 to Heller et al.

[0310] In preferred embodiments, the working electrode comprises a wire
formed from a conductive material, such as platinum, platinum-iridium,
palladium, graphite, gold, carbon, conductive polymer, alloys, and the
like. Although the electrodes can by formed by a variety of manufacturing
techniques (bulk metal processing, deposition of metal onto a substrate,
and the like), it can be advantageous to form the electrodes from plated
wire (e.g., platinum on steel wire) or bulk metal (e.g., platinum wire).
It is believed that electrodes formed from bulk metal wire provide
superior performance (e.g., in contrast to deposited electrodes),
including increased stability of assay, simplified manufacturability,
resistance to contamination (e.g., which can be introduced in deposition
processes), and improved surface reaction (e.g., due to purity of
material) without peeling or delamination.

[0311] The working electrode 122 is configured to measure the
concentration of an analyte. In an enzymatic electrochemical sensor for
detecting glucose, for example, the working electrode measures the
hydrogen peroxide produced by an enzyme catalyzed reaction of the analyte
being detected and creates a measurable electronic current. For example,
in the detection of glucose wherein glucose oxidase produces hydrogen
peroxide as a byproduct, hydrogen peroxide reacts with the surface of the
working electrode producing two protons (2H.sup.+), two electrons
(2e.sup.-) and one molecule of oxygen (O2), which produces the
electronic current being detected.

[0312] In preferred embodiments, the working electrode 122 is covered with
an insulating material 126, for example, a non-conductive polymer.
Dip-coating, spray-coating, vapor-deposition, or other coating or
deposition techniques can be used to deposit the insulating material on
the working electrode. In one embodiment, the insulating material
comprises parylene, which can be an advantageous polymer coating for its
strength, lubricity, and electrical insulation properties. Generally,
parylene is produced by vapor deposition and polymerization of
para-xylylene (or its substituted derivatives). However, any suitable
insulating material can be used, for example, fluorinated polymers,
polyethyleneterephthalate, polyurethane, polyimide, other nonconducting
polymers, and the like. Glass or ceramic materials can also be employed.
Other materials suitable for use include surface energy modified coating
systems such as are marketed under the trade names AMC18, AMC148, AMC141,
and AMC321 by Advanced Materials Components Express of Bellafonte, Pa. In
some alternative embodiments, however, the working electrode may not
require a coating of insulator.

[0313] The reference electrode 124, which can function as a reference
electrode alone, or as a dual reference and counter electrode, is formed
from silver, silver/silver chloride, and the like. Preferably, the
reference electrode 124 is juxtapositioned and/or twisted with or around
a wire 122 that forms the working electrode 128; however other
configurations are also possible. In the illustrated embodiments, the
reference electrode 124 is helically wound around the wire 122. The
assembly of wires is then optionally coated or adhered together with an
insulating material, similar to that described above, so as to provide an
insulating attachment.

[0314] In embodiments wherein an outer insulator is disposed, a portion of
the coated assembly structure can be stripped or otherwise removed, for
example, by hand, excimer lasing, chemical etching, laser ablation,
grit-blasting (e.g., with sodium bicarbonate or other suitable grit), and
the like, to expose the electroactive surfaces. Alternatively, a portion
of the electrode can be masked prior to depositing the insulator in order
to maintain an exposed electroactive surface area.

[0315] In the embodiment illustrated in FIG. 1B, a radial window is formed
through the insulating material 126 to expose a circumferential
electroactive surface of the working electrode 128. Additionally,
sections 129 of electroactive surface of the reference electrode are
exposed. For example, the 129 sections of electroactive surface can be
masked during deposition of an outer insulating layer or etched after
deposition of an outer insulating layer.

[0316] In some alternative embodiments, additional electrodes can be
included within the assembly, for example, a three-electrode system
(working, reference, and counter electrodes) and/or an additional working
electrode (e.g., an electrode which can be used to generate oxygen, which
is configured as a baseline subtracting electrode, or which is configured
for measuring additional analytes) as described in more detail elsewhere
herein. U.S. Patent Publication No. US-2005-0161346-A1 and U.S. Patent
Publication No. US-2005-0143635-A1 describe some systems and methods for
implementing and using additional working, counter, and/or reference
electrodes.

[0317] Preferably, a membrane system is deposited over the electroactive
surfaces of the sensor 100B and includes a plurality of domains or
layers. The membrane system may be deposited on the exposed electroactive
surfaces using known thin film techniques (for example, spraying,
electro-depositing, dipping, and the like). In one exemplary embodiment,
each domain is deposited by dipping the sensor into a solution and
drawing out the sensor at a speed that provides the appropriate domain
thickness. In general, the membrane system may be disposed over (e.g.,
deposited on) the electroactive surfaces using methods appreciated by one
skilled in the art.

[0318] In some embodiments, the sensing membranes and/or membrane systems
include a plurality of domains or layers, for example, an interference
domain, an enzyme domain, and a resistance domain, and may include
additional domains, such as an electrode domain, a cell impermeable
domain (also referred to as a bioprotective layer), and/or an oxygen
domain, as described in more detail in co-pending U.S. patent application
Ser. No. 11/750,907 filed on May 18, 2007 and entitled "ANALYTE SENSORS
HAVING A SIGNAL-TO-NOISE RATIO SUBSTANTIALLY UNAFFECTED BY NON-CONSTANT
NOISE," which is incorporated herein by reference in its entirety.
However, it is understood that a sensing membrane modified for other
sensors, for example, by including fewer or additional domains is within
the scope of some embodiments. In some embodiments, one or more domains
of the sensing membranes are formed from materials such as silicone,
polytetrafluoroethylene, polyethylene-co-tetrafluoroethylene, polyolefin,
polyester, polycarbonate, biostable polytetrafluoroethylene,
homopolymers, copolymers, terpolymers of polyurethanes, polypropylene
(PP), polyvinylchloride (PVC), polyvinylidene fluoride (PVDF),
polybutylene terephthalate (PBT), polymethylmethacrylate (PMMA),
polyether ether ketone (PEEK), polyurethanes, cellulosic polymers,
poly(ethylene oxide), poly(propylene oxide) and copolymers and blends
thereof, polysulfones and block copolymers thereof including, for
example, di-block, tri-block, alternating, random and graft copolymers.
U.S. Patent Publication No. US-2005-024579912-A1, which is incorporated
herein by reference in its entirety, describes biointerface and sensing
membrane configurations and materials that may be applied to some
embodiments.

[0319] In one exemplary embodiment, the sensor is an enzyme-based
electrochemical sensor, wherein the glucose-measuring working electrode
measures the hydrogen peroxide produced by the enzyme catalyzed reaction
of glucose being detected and creates a measurable electronic current
(for example, detection of glucose utilizing glucose oxidase produces
H2O2 peroxide as a byproduct, H2O2 reacts with the
surface of the working electrode producing two protons (2H.sup.+), two
electrons (2e.sup.-) and one molecule of oxygen (O2) which produces
the electronic current being detected), such as described in more detail
above and as is appreciated by one skilled in the art. Typically, the
working and reference electrodes operatively connect with sensor
electronics, such as described in more detail elsewhere herein.
Additional aspects of the above-described transcutaneously inserted
sensor can be found in co-pending U.S. Patent Publication No.
US-2006-0020187-A1.

[0320] FIG. 1C is a cross-sectional schematic view of a sensing region of
a dual-electrode analyte sensor in one embodiment wherein an active
enzyme of an enzyme domain is positioned over the first working electrode
but not over the second working electrode, such as described with
reference to U.S. patent application Ser. No. 12/055,149, which is
incorporated herein by reference in its entirety. In general,
electrochemical analyte sensors provide at least one working electrode
and at least one reference electrode, which are configured to generate a
signal associated with a concentration of the analyte in the host, such
as described herein, and as appreciated by one skilled in the art. The
output signal is typically a raw data stream that is used to provide a
useful value of the measured analyte concentration in a host to the
patient or doctor, for example. However, the analyte sensors of the
preferred embodiments may further measure at least one additional signal.
For example, in some embodiments, the additional signal is associated
with the baseline and/or sensitivity of the analyte sensor, thereby
enabling monitoring of baseline and/or sensitivity changes that may occur
in a continuous analyte sensor over time.

[0321] In preferred embodiments, the analyte sensor comprises a first
working electrode E1 and a second working electrode E2, in addition to a
reference electrode, which is referred to as a dual-electrode system
herein. The first and second working electrodes may be in any useful
conformation, as described in US Patent Publications Nos.
US-2007-0027385-A1, US-2007-0213611-A1, US-2007-0027284-A1,
US-2007-0032717-A1, US-2007-0093704, and U.S. patent application Ser. No.
11/865,572 filed on Oct. 1, 2007 and entitled "DUAL-ELECTRODE SYSTEM FOR
A CONTINUOUS ANALYTE SENSOR," each of which is incorporated herein by
reference in its entirety. In some preferred embodiments, the first and
second working electrodes are twisted and/or bundled. For example, two
wire working electrodes can be twisted together, such as in a helix
conformation. The reference electrode can then be wrapped around the
twisted pair of working electrodes. In some preferred embodiments, the
first and second working electrodes include a coaxial configuration. A
variety of dual-electrode system configurations are described with
reference to FIGS. 7A1 through 11 of the references incorporated above,
for example. In some embodiments, the sensor is configured as a dual
electrode sensor, such as described in US Patent Publication Nos.
US-2005-0143635-A1; US-2007-0027385-A1; and US-2007-0213611-A1, and
co-pending U.S. patent application Ser. No. 11/865,572, each of which is
incorporated herein by reference in its entirety. However, a
dual-electrode system can be provided in any planar or non-planar
configuration, such as can be appreciated by one skilled in the art, and
can be found in U.S. Pat. No. 6,175,752 to Say et al.; U.S. Pat. No.
6,579,690 to Bonnecaze et al.; U.S. Pat. No. 6,484,046 to Say et al.;
U.S. Pat. No. 6,512,939 to Colvin et al.; U.S. Pat. No. 6,477,395 to
Schulman et al.; U.S. Pat. No. 6,424,847 to Mastrototaro et al.; U.S.
Pat. No. 6,212,416 to Ward et al.; U.S. Pat. No. 6,119,028 to Schulman et
al.; U.S. Pat. No. 6,400,974 to Lesho; U.S. Pat. No. 6,595,919 to Berner
et al.; U.S. Pat. No. 6,141,573 to Kurnik et al.; U.S. Pat. No. 6,122,536
to Sun et al.; European Patent Application EP 1153571 to Varall et al.;
U.S. Pat. No. 6,512,939 to Colvin et al.; U.S. Pat. No. 5,605,152 to
Slate et al.; U.S. Pat. No. 4,431,004 to Bessman et al.; U.S. Pat. No.
4,703,756 to Gough et al.; U.S. Pat. No. 6,514,718 to Heller et al.; U.S.
Pat. No. 5,985,129 to Gough et al.; WO Patent Application Publication No.
04/021877 to Caduff; U.S. Pat. No. 5,494,562 to Maley et al.; U.S. Pat.
No. 6,120,676 to Heller et al.; and U.S. Pat. No. 6,542,765 to Guy et
al., each of which are incorporated in their entirety herein by reference
in their entirety. In general, it is understood that the disclosed
embodiments are applicable to a variety of continuous analyte measuring
device configurations

[0322] The dual-electrode sensor system includes a first working electrode
E1 and the second working electrode E2, both of which are disposed
beneath a sensor membrane M02. The first working electrode E1 is disposed
beneath an active enzymatic portion M04 of the sensor membrane M02, which
includes an enzyme configured to detect the analyte or an analyte-related
compound. Accordingly, the first working electrode E1 is configured to
generate a first signal composed of both signal related to the analyte
and signal related to non-analyte electroactive compounds (e.g.,
physiological baseline, interferents, and non-constant noise) that have
an oxidation/reduction potential that overlaps with the
oxidation/reduction potential of the analyte. This oxidation/reduction
potential may be referred to as a "first oxidation/reduction potential"
herein. The second working electrode E2 is disposed beneath an
inactive-enzymatic or non-enzymatic portion M06 of the sensor membrane
M02. The non-enzymatic portion M06 of the membrane includes either an
inactivated form of the enzyme contained in the enzymatic portion M04 of
the membrane or no enzyme. In some embodiments, the non-enzymatic portion
M06 can include a non-specific protein, such as BSA, ovalbumin, milk
protein, certain polypeptides, and the like. The non-enzymatic portion
M06 generates a second signal associated with noise of the analyte
sensor. The noise of the sensor comprises signal contribution due to
non-analyte electroactive species (e.g., interferents) that have an
oxidation/reduction potential that substantially overlaps the first
oxidation/reduction potential (e.g., that overlap with the
oxidation/reduction potential of the analyte). In some embodiments of a
dual-electrode analyte sensor configured for fluid communication with a
host's circulatory system, the non-analyte related electroactive species
comprises at least one species selected from the group consisting of
interfering species, non-reaction-related hydrogen peroxide, and other
electroactive species.

[0323] In one exemplary embodiment, the dual-electrode analyte sensor is a
glucose sensor having a first working electrode E1 configured to generate
a first signal associated with both glucose and non-glucose related
electroactive compounds that have a first oxidation/reduction potential.
Non-glucose related electroactive compounds can be any compound, in the
sensor's local environment that has an oxidation/reduction potential
substantially overlapping with the oxidation/reduction potential of
H2O2, for example. While not wishing to be bound by theory, it
is believed that the glucose-measuring electrode can measure both the
signal directly related to the reaction of glucose with GOx (produces
H2O2 that is oxidized at the working electrode) and signals
from unknown compounds that are in the tissue or blood surrounding the
sensor. These unknown compounds can be constant or non-constant (e.g.,
intermittent or transient) in concentration and/or effect. In some
circumstances, it is believed that some of these unknown compounds are
related to the host's disease state. For example, it is known that
tissue/blood chemistry changes dramatically during/after a heart attack
(e.g., pH changes, changes in the concentration of various blood
components/protein, and the like). Additionally, a variety of medicaments
or infusion fluid components (e.g., acetaminophen, ascorbic acid,
dopamine, ibuprofen, salicylic acid, tolbutamide, tetracycline,
creatinine, uric acid, ephedrine, L-dopa, methyl dopa and tolazamide)
that may be given to the host may have oxidation/reduction potentials
that overlap with that of H2O2.

[0324] In this exemplary embodiment, the dual-electrode analyte sensor
includes a second working electrode E2 that is configured to generate a
second signal associated with the non-glucose related electroactive
compounds that have the same oxidation/reduction potential as the
above-described first working electrode (e.g., para supra). In some
embodiments, the non-glucose related electroactive species includes at
least one of interfering species, non-reaction-related H2O2,
and other electroactive species. For example, interfering species
includes any compound that is not directly related to the electrochemical
signal generated by the glucose-GOx reaction, such as but not limited to
electroactive species in the local environment produces by other bodily
processes (e.g., cellular metabolism, a disease process, and the like).
Other electroactive species includes any compound that has an
oxidation/reduction potential similar to or overlapping that of
H2O2.

[0325] The non-analyte (e.g., non-glucose) signal produced by compounds
other than the analyte (e.g., glucose) obscures the signal related to the
analyte, contributes to sensor inaccuracy, and is considered background
noise. Background noise includes both constant and non-constant
components and must be removed to accurately calculate the analyte
concentration. While not wishing to be bound by theory, it is believed
that preferred dual electrode sensors are designed such that the first
and second electrodes are influenced by substantially the same
external/environmental factors, which enables substantially equivalent
measurement of both the constant and non-constant species/noise. This
advantageously allows the substantial elimination of noise on the sensor
signal (using electronics described elsewhere herein) to substantially
reduce or eliminate signal effects due to noise, including non-constant
noise (e.g., unpredictable biological, biochemical species, medicaments,
pH fluctuations, O2 fluctuations, or the like) known to effect the
accuracy of conventional continuous sensor signals. Preferably, the
sensor includes electronics operably connected to the first and second
working electrodes. The electronics are configured to provide the first
and second signals that are used to generate glucose concentration data
substantially without signal contribution due to non-glucose-related
noise. Preferably, the electronics include at least a potentiostat that
provides a bias to the electrodes. In some embodiments, sensor
electronics are configured to measure the current (or voltage) to provide
the first and second signals. The first and second signals are used to
determine the glucose concentration substantially without signal
contribution due to non-glucose-related noise such as by but not limited
to subtraction of the second signal from the first signal or alternative
data analysis techniques. In some embodiments, the sensor electronics
include a transmitter that transmits the first and second signals to a
receiver, where additional data analysis and/or calibration of glucose
concentration can be processed. U.S. Patent Publication No.
US-2005-0027463-A1, US-2005-0203360-A1 and U.S. Patent Publication No.
US-2006-0036142-A1 describes systems and methods for processing sensor
analyte data and is incorporated herein by reference in their entirety.

[0326] In preferred embodiments, the dual-electrode sensor includes
electronics (e.g., a processor module, processing memory) that are
operably connected to the first and second working electrodes and are
configured to provide the first and second signals to generate analyte
concentration data substantially without signal contribution due to
non-analyte-related noise. For example, the sensor electronics process
and/or analyze the signals from the first and second working electrodes
and calculate the portion of the first electrode signal that is due to
analyte concentration only. The portion of the first electrode signal
that is not due to the analyte concentration can be considered to be
background, such as but not limited to noise. Accordingly, in one
embodiment of a dual-electrode sensor system configured for fluid
communication with a host's circulatory system (e.g., via a vascular
access device) the system comprising electronics operably connected to
the first and second working electrodes; the electronics are configured
to process the first and second signals to generate analyte concentration
data substantially without signal contribution due to noise.

[0327] In various embodiments, the electrodes can be stacked or grouped
similar to that of a leaf spring configuration, wherein layers of
electrode and insulator (or individual insulated electrodes) are stacked
in offset layers. The offset layers can be held together with bindings of
non-conductive material, foil, or wire. As is appreciated by one skilled
in the art, the strength, flexibility, and/or other material property of
the leaf spring-configured or stacked sensor can be either modified
(e.g., increased or decreased), by varying the amount of offset, the
amount of binding, thickness of the layers, and/or materials selected and
their thicknesses, for example.

[0328] In preferred embodiments, the analyte sensor substantially
continuously measures the host's analyte concentration. In some
embodiments, for example, the sensor can measure the analyte
concentration every fraction of a second, about every fraction of a
minute or every minute. In other exemplary embodiments, the sensor
measures the analyte concentration about every 2, 3, 4, 5, 6, 7, 8, 9, or
10 minutes. In still other embodiments, the sensor measures the analyte
concentration every fraction of an hour, such as but not limited to every
15, 30 or 45 minutes. Yet in other embodiments, the sensor measures the
analyte concentration about every hour or longer. In some exemplary
embodiments, the sensor measures the analyte concentration intermittently
or periodically. In one preferred embodiment, the analyte sensor is a
glucose sensor and measures the host's glucose concentration about every
4-6 minutes. In a further embodiment, the sensor measures the host's
glucose concentration every 5 minutes.

[0329] In some embodiments (e.g., sensors such as illustrated in FIGS. 1A,
1B, and 1C), a potentiostat is employed to monitor the electrochemical
reaction at the electrochemical cell. The potentiostat applies a constant
potential to the working and reference electrodes to determine a current
value. The current that is produced at the working electrode (and flows
through the circuitry to the counter electrode) is proportional to the
amount of H2O2 that diffuses to the working electrode.
Accordingly, a raw signal can be produced that is representative of the
concentration of glucose in the user's body, and therefore can be
utilized to estimate a meaningful glucose value, such as described
herein.

[0330] One problem with raw data stream output of some enzymatic glucose
sensors such as described above is caused by transient non-glucose
reaction rate-limiting phenomena. For example, if oxygen is deficient,
relative to the amount of glucose, then the enzymatic reaction will be
limited by oxygen rather than glucose. Consequently, the output signal
will be indicative of the oxygen concentration rather than the glucose
concentration, producing erroneous signals. Other non-glucose reaction
rate-limiting phenomena could include interfering species, temperature
and/or pH changes, or even unknown sources of mechanical, electrical
and/or biochemical noise, for example.

[0331]FIG. 2 is a block diagram that illustrates one possible
configuration of the sensor electronics 200 in one embodiment. In this
embodiment, a potentiostat 210 is shown, which is operatively connected
to an electrode system (FIG. 1A or 1B) and provides a voltage to the
electrodes, which biases the sensor to enable measurement of a current
value indicative of the analyte concentration in the host (also referred
to as the analog portion). In some embodiments, the potentiostat includes
a resistor (not shown) that translates the current into voltage. In some
alternative embodiments, a current to frequency converter is provided
that is configured to continuously integrate the measured current, for
example, using a charge counting device. In the illustrated embodiment,
an A/D converter 212 digitizes the analog signal into "counts" for
processing. Accordingly, the resulting raw data stream in counts is
directly related to the current measured by the potentiostat 210.

[0332] A processor module 214 is the central control unit that controls
the processing of the sensor electronics. In some embodiments, the
processor module includes a microprocessor, however a computer system
other than a microprocessor can be used to process data as described
herein, for example an ASIC can be used for some or all of the sensor's
central processing. The processor typically provides semi-permanent
storage of data, for example, storing data such as sensor identifier (ID)
and programming to process data streams (for example, programming for
data smoothing and/or replacement of signal artifacts such as is
described in more detail elsewhere herein). The processor additionally
can be used for the system's cache memory, for example for temporarily
storing recent sensor data. In some embodiments, the processor module
comprises memory storage components such as ROM, RAM, dynamic-RAM,
static-RAM, non-static RAM, EEPROM, rewritable ROMs, flash memory, and
the like. In one exemplary embodiment, ROM 216 provides semi-permanent
storage of data, for example, storing data such as sensor identifier (ID)
and programming to process data streams (e.g., programming for signal
artifacts detection and/or replacement such as described elsewhere
herein). In one exemplary embodiment, RAM 218 can be used for the
system's cache memory, for example for temporarily storing recent sensor
data.

[0333] In some embodiments, the processor module comprises a digital
filter, for example, an IIR or FIR filter, configured to smooth the raw
data stream from the A/D converter. Generally, digital filters are
programmed to filter data sampled at a predetermined time interval (also
referred to as a sample rate). In some embodiments, wherein the
potentiostat is configured to measure the analyte at discrete time
intervals, these time intervals determine the sample rate of the digital
filter. In some alternative embodiments, wherein the potentiostat is
configured to continuously measure the analyte, for example, using a
current-to-frequency converter, the processor module can be programmed to
request a digital value from the A/D converter at a predetermined time
interval, also referred to as the acquisition time. In these alternative
embodiments, the values obtained by the processor are advantageously
averaged over the acquisition time due the continuity of the current
measurement. Accordingly, the acquisition time determines the sample rate
of the digital filter. In preferred embodiments, the processor module is
configured with a programmable acquisition time, namely, the
predetermined time interval for requesting the digital value from the A/D
converter is programmable by a user within the digital circuitry of the
processor module. An acquisition of time from about 2 seconds to about
512 seconds is preferred; however any acquisition time can be programmed
into the processor module. A programmable acquisition time is
advantageous in optimizing noise filtration, time lag, and
processing/battery power.

[0334] In some embodiments, the processor module is configured to build
the data packet for transmission to an outside source, for example, an RF
transmission to a receiver as described in more detail below. Generally,
the data packet comprises a plurality of bits that can include a
sensor/transmitter ID code, raw data, filtered data, and/or error
detection or correction. The processor module can be configured to
transmit any combination of raw and/or filtered data.

[0335] A battery 220 is operatively connected to the processor 214 and
provides the necessary power for the sensor (e.g., 100A or 100B). In one
embodiment, the battery is a Lithium Manganese Dioxide battery, however
any appropriately sized and powered battery can be used (e.g., AAA,
Nickel-cadmium, Zinc-carbon, Alkaline, Lithium, Nickel-metal hydride,
Lithium-ion, Zinc-air, Zinc-mercury oxide, Silver-zinc, or
hermetically-sealed). In some embodiments the battery is rechargeable. In
some embodiments, a plurality of batteries can be used to power the
system. In yet other embodiments, the receiver can be transcutaneously
powered via an inductive coupling, for example. A Quartz Crystal 222 is
operatively connected to the processor 214 and maintains system time for
the computer system as a whole.

[0336] An optional RF module (e.g., an RF Transceiver) 224 is operably
connected to the processor 214 and transmits the sensor data from the
sensor (e.g., 100A or 100B) to a receiver (see FIGS. 3 and 4). Although
an RF transceiver is shown here, some other embodiments can include a
wired rather than wireless connection to the receiver. A second quartz
crystal 226 provides the system time for synchronizing the data
transmissions from the RF transceiver. It is noted that the transceiver
224 can be substituted with a transmitter in other embodiments. In some
alternative embodiments, however, other mechanisms, such as optical,
infrared radiation (IR), ultrasonic, and the like, can be used to
transmit and/or receive data.

[0337] In some embodiments, a Signal Artifacts Detector 228 is provided
that includes one or more of the following: an oxygen detector 228a, a pH
detector 228b, a temperature detector 228c, and a pressure/stress
detector 228d, which is described in more detail with reference to signal
artifacts detection. It is noted that in some embodiments the signal
artifacts detector 228 is a separate entity (e.g., temperature detector)
operatively connected to the processor, while in other embodiments, the
signal artifacts detector is a part of the processor and utilizes
readings from the electrodes, for example, to detect ischemia and other
signal artifacts. Although the above description is focused on an
embodiment of the Signal Artifacts Detector within the sensor, some
embodiments provide for systems and methods for detecting signal
artifacts in the sensor and/or receiver electronics (e.g., processor
module) as described in more detail elsewhere herein.

Receiver

[0338] FIGS. 3A to 3D are schematic views of a receiver 300 including
representations of estimated glucose values on its user interface in
first, second, third, and fourth embodiments, respectively. The receiver
300 comprises systems to receive, process, and display sensor data from
the glucose sensor (e.g., 100A or 100B), such as described herein.
Particularly, the receiver 300 can be a pager-sized device, for example,
and comprise a user interface that has a plurality of buttons 302 and a
liquid crystal display (LCD) screen 304, and which can optionally include
a backlight. In some embodiments, the user interface can also include a
keyboard, a speaker, and a vibrator, as described below with reference to
FIG. 4A.

[0339] FIG. 3A illustrates a first embodiment wherein the receiver 300
shows a numeric representation of the estimated glucose value on its user
interface, which is described in more detail elsewhere herein.

[0340]FIG. 3B illustrates a second embodiment wherein the receiver 300
shows an estimated glucose value and approximately one hour of historical
trend data on its user interface, which is described in more detail
elsewhere herein.

[0341]FIG. 3c illustrates a third embodiment wherein the receiver 300
shows an estimated glucose value and approximately three hours of
historical trend data on its user interface, which is described in more
detail elsewhere herein.

[0342] FIG. 3D illustrates a fourth embodiment wherein the receiver 300
shows an estimated glucose value and approximately nine hours of
historical trend data on its user interface, which is described in more
detail elsewhere herein.

[0343] In some embodiments, a user can toggle through some or all of the
screens shown in FIGS. 3A to 3D using a toggle button on the receiver. In
some embodiments, the user will be able to interactively select the type
of output displayed on their user interface. In other embodiments, the
sensor output can have alternative configurations.

[0344]FIG. 4A is a block diagram that illustrates one possible
configuration of the receiver's electronics. It is noted that the
receiver 300 can comprise a configuration such as described with
reference to FIGS. 3A to 3D, above. Alternatively, the receiver 300 can
comprise other configurations, including a phone, insulin pump, desktop
computer, laptop computer, a personal digital assistant (PDA), a server
(local or remote to the receiver), and the like. In some embodiments, the
receiver 300 can be adapted to connect (via wired or wireless connection)
to a phone, insulin pump, desktop computer, laptop computer, PDA, server
(local or remote to the receiver), and the like, in order to download
data from the receiver 300. In some alternative embodiments, the receiver
300 and/or receiver electronics can be housed within or directly
connected to the sensor (e.g., 100A or 100B) in a manner that allows
sensor and receiver electronics to work directly together and/or share
data processing resources. Accordingly, the receiver's electronics can be
generally referred to as a "computer system."

[0345] A quartz crystal 402 is operatively connected to an optional RF
transceiver 404 that together function to receive and synchronize data
streams (e.g., raw data streams transmitted from the RF transceiver).
Once received, whether via wired or wireless transmission, a processor
406 processes the signals, such as described below.

[0346] The processor 406, also referred to as the processor module, is the
central control unit that performs the processing, such as storing data,
analyzing data streams, calibrating analyte sensor data, detecting signal
artifacts, classifying a level of noise, calculating a rate of change,
predicting analyte values, setting of modes, estimating analyte values,
comparing estimated analyte values with time corresponding measured
analyte values, analyzing a variation of estimated analyte values,
downloading data, and controlling the user interface by providing analyte
values, prompts, messages, warnings, alarms, and the like. The processor
includes hardware and software that performs the processing described
herein, for example flash memory provides permanent or semi-permanent
storage of data, storing data such as sensor ID, receiver ID, and
programming to process data streams (for example, programming for
performing estimation and other algorithms described elsewhere herein)
and random access memory (RAM) stores the system's cache memory and is
helpful in data processing.

[0347] In one exemplary embodiment, the processor is a microprocessor that
provides the processing, such as calibration algorithms stored within a
ROM 408. The ROM 408 is operatively connected to the processor 406 and
provides semi-permanent storage of data, storing data such as receiver ID
and programming to process data streams (e.g., programming for performing
calibration and other algorithms described elsewhere herein). In this
exemplary embodiment, an RAM 410 is used for the system's cache memory
and is helpful in data processing. The term "processor module" can
include some portions or all of ROM 408 and RAM 410 in addition to the
processor 406.

[0348] A battery 412 is operatively connected to the processor 406 and
provides power for the receiver. In one embodiment, the battery is a
standard AAA alkaline battery, however any appropriately sized and
powered battery can be used. In some embodiments, a plurality of
batteries can be used to power the system. A quartz crystal 414 is
operatively connected to the processor 406 and maintains system time for
the computer system as a whole.

[0349] A user interface 416 comprises a keyboard 416a, speaker 416b,
vibrator 416c, backlight 416d, liquid crystal display (LCD 416e), and one
or more buttons 416f. The components that comprise the user interface 416
provide controls to interact with the user. The keyboard 416a can allow,
for example, input of user information about himself/herself, such as
mealtime, exercise, insulin administration, and reference glucose values.
The speaker 416b can provide, for example, audible signals or alerts for
conditions such as present and/or predicted hyper- and hypoglycemic
conditions. The vibrator 416c can provide, for example, tactile signals
or alerts for reasons such as described with reference to the speaker,
above. The backlight 416d can be provided, for example, to aid the user
in reading the LCD in low light conditions. The LCD 416e can be provided,
for example, to provide the user with visual data output such as is
illustrated in FIGS. 3A to 3D. The buttons 416f can provide for toggle,
menu selection, option selection, mode selection, and reset, for example.

[0350] In some embodiments, prompts or messages can be displayed on the
user interface to convey information to the user, such as reference
outlier values, requests for reference analyte values, therapy
recommendations, deviation of the measured analyte values from the
estimated analyte values, and the like. Additionally, prompts can be
displayed to guide the user through calibration or trouble-shooting of
the calibration.

[0351] Output can be provided via a user interface 416, including but not
limited to, visually on a screen 416e, audibly through a speaker 416b, or
tactilely through a vibrator 416c. Additionally, output can be provided
via wired or wireless connection to an external device, including but not
limited to, phone, computer, laptop, server, personal digital assistant,
modem connection, insulin delivery mechanism, medical device, or other
device that can be useful in interfacing with the receiver.

[0352] Output can be continuously provided, or certain output can be
selectively provided based on modes, events, analyte concentrations and
the like. For example, an estimated analyte path can be continuously
provided to a patient on an LCD screen 416e, while audible alerts can be
provided only during a time of existing or approaching clinical risk to a
patient. As another example, estimation can be provided based on event
triggers (for example, when an analyte concentration is nearing or
entering a clinically risky zone). As yet another example, analyzed
deviation of estimated analyte values can be provided when a
predetermined level of variation (for example, due to known error or
clinical risk) is known.

[0353] In some embodiments, alarms prompt or alert a patient when a
measured or projected analyte value or rate of change simply passes a
predetermined threshold. In some embodiments, the clinical risk alarms
combine intelligent and dynamic estimative algorithms to provide greater
accuracy, more timeliness in pending danger, avoidance of false alarms,
and less annoyance for the patient. For example, clinical risk alarms of
these embodiments include dynamic and intelligent estimative algorithms
based on analyte value, rate of change, acceleration, clinical risk,
statistical probabilities, known physiological constraints, and/or
individual physiological patterns, thereby providing more appropriate,
clinically safe, and patient-friendly alarms.

[0354] In some embodiments, at least one of a hypoglycemia, hyperglycemia,
predicted hypoglycemia, and predicted hyperglycemia alarm includes first
and second user selectable alarms. In some embodiments, the first alarm
is configured to alarm during a first time of day and wherein the second
alarm is configured to alarm during a second time of day (for example, so
that a host can set different alarm settings for day vs. night, avoiding
unnecessary night-time alarming). In some embodiments, the alarm is
configured to turn on a light. In some embodiments, the alarm is
configured to alarm a remote receiver located more than about 10 feet
away from the continuous glucose sensor (for example, in a parent's
bedroom or to a health care provider). In some embodiments, the alarm
comprises a text message, and wherein the computer system is configured
to send the text message to a remote device. Accordingly, alarms and
other system processing can be set by modes of the system, such as
described in more detail elsewhere herein.

[0355] In some embodiments, clinical risk alarms can be activated for a
predetermined time period to allow for the user to attend to his/her
condition. Additionally, the clinical risk alarms can be de-activated
when leaving a clinical risk zone so as not to annoy the patient by
repeated clinical risk alarms, when the patient's condition is improving.

[0356] In some embodiments, the system determines a possibility of the
patient avoiding clinical risk, based on the analyte concentration, the
rate of change, and other aspects of the sensor algorithms. If there is
minimal or no possibility of avoiding the clinical risk, a clinical risk
alarm will be triggered. However, if there is a possibility of avoiding
the clinical risk, the system can wait a predetermined amount of time and
re-analyze the possibility of avoiding the clinical risk. In some
embodiments, when there is a possibility of avoiding the clinical risk,
the system will further provide targets, therapy recommendations, or
other information that can aid the patient in proactively avoiding the
clinical risk.

[0357] In some embodiments, a variety of different display methods are
used, such as described in the preferred embodiments, which can be
toggled through or selectively displayed to the user based on conditions
or by selecting a button, for example. As one example, a simple screen
can be normally shown that provides an overview of analyte data, for
example present analyte value and directional trend. More complex screens
can then be selected when a user desires more detailed information, for
example, historical analyte data, alarms, clinical risk zones, and the
like.

[0358] In some embodiments, electronics 422 associated with a medicament
delivery device 502 are operably connected to the processor 406 and
include a processor 424 for processing data associated with the delivery
device 502 and include at least a wired or wireless connection (for
example, RF transceiver) 426 for transmission of data between the
processor 406 of the receiver 300 and the processor 424 of the delivery
device 502. Other electronics associated with any of the delivery devices
cited herein, or other known delivery devices, may be implemented with
the delivery device electronics 422 described herein, as is appreciated
by one skilled in the art. In some embodiments, type, amount, validation
and other processing related to medicament delivery is based at least in
part on a mode of the system, which is described in more detail elsewhere
herein.

[0359] In some embodiments, the processor 424 comprises programming for
processing the delivery information in combination with the continuous
sensor information. In some alternative embodiments, the processor 406
comprises programming for processing the delivery information in
combination with the continuous sensor information. In some embodiments,
both processors 406 and 422 mutually process information related to each
component.

[0360] In some embodiments, the medicament delivery device 502 further
includes a user interface (not shown), which may include a display and/or
buttons, for example. U.S. Pat. Nos. 6,192,891, 5,536,249, and 6,471,689
describe some examples of incorporation of a user interface into a
medicament delivery device, as is appreciated by one skilled in the art.

[0361] In some embodiments, electronics 428 associated with the single
point glucose monitor 428 are operably connected to a processor 432 and
include a potentiostat 430 in one embodiment that measures a current flow
produced at the working electrode when a biological sample is placed on a
sensing membrane, such as described above. The single point glucose
monitor 428 can include at least one of a wired and a wireless connection
434.

[0362] FIG. 4B is an illustration of the receiver in one embodiment
showing an analyte trend graph, including measured analyte values,
estimated analyte values, and a clinical risk zone. The receiver 300
includes an LCD screen 304, buttons 302, and a speaker 416d and/or
microphone. The screen 304 displays a trend graph in the form of a line
representing the historical trend of a patient's analyte concentration.
Although axes may or may not be shown on the screen 304, it is understood
that a theoretical x-axis represents time and a theoretical y-axis
represents analyte concentration.

[0363] In some embodiments such as shown in FIG. 4B, the screen shows
thresholds, including a high threshold 440 and a low threshold 442, which
represent boundaries between clinically safe and clinically risky
conditions for the patients. In one exemplary embodiment, a normal
glucose threshold for a glucose sensor is set between about 100 and 160
mg/dL, and the clinical risk zones 444 are illustrated outside of these
thresholds. In alternative embodiments, the normal glucose threshold is
between about 80 and about 200 mg/dL, between about 55 and about 220
mg/dL, or other threshold that can be set by the manufacturer, physician,
patient, computer program, and the like. Although a few examples of
glucose thresholds are given for a glucose sensor, the setting of any
analyte threshold is not limited by the preferred embodiments, including
rate of change and/or acceleration information. In some embodiments, one
or more criteria that define clinical risk and/or alarms are based at
least in part on a mode of the system, which is described in more detail
elsewhere herein.

[0364] In some embodiments, the screen 304 shows clinical risk zones 444,
also referred to as danger zones, through shading, gradients, or other
graphical illustrations that indicate areas of increasing clinical risk.
Clinical risk zones 444 can be set by a manufacturer, customized by a
doctor, and/or set by a user via buttons 302, for example. In some
embodiments, the danger zone 444 can be continuously shown on the screen
304, or the danger zone can appear when the measured and/or estimated
analyte values fall into the danger zone 444. Additional information can
be displayed on the screen, such as an estimated time to clinical risk.
In some embodiments, the danger zone can be divided into levels of danger
(for example, low, medium, and high) and/or can be color-coded (for
example, yellow, orange, and red) or otherwise illustrated to indicate
the level of danger to the patient. Additionally, the screen or portion
of the screen can dynamically change colors or illustrations that
represent a nearness to the clinical risk and/or a severity of clinical
risk.

[0365] In some embodiments, such as shown in FIG. 4B, the screen 304
displays a trend graph of measured analyte data 446. Measured analyte
data can be smoothed and calibrated such as described in more detail
elsewhere herein. Measured analyte data can be displayed for a certain
time period (for example, previous 1 hour, 3 hours, 9 hours, etc.) In
some embodiments, the user can toggle through screens using buttons 302
to view the measured analyte data for different time periods, using
different formats, or to view certain analyte values (for example, highs
and lows).

[0366] In some embodiments such as shown in FIG. 4B, the screen 304
displays estimated analyte data 448 using dots. In this illustration, the
size of the dots can represent the confidence of the estimation, a
variation of estimated values, and the like. For example, as the time
gets farther away from the present (t=0) the confidence level in the
accuracy of the estimation can decline as is appreciated by one skilled
in the art. In some alternative embodiments, dashed lines, symbols,
icons, and the like can be used to represent the estimated analyte
values. In some alternative embodiments, shaded regions, colors,
patterns, and the like can also be used to represent the estimated
analyte values, a confidence in those values, and/or a variation of those
values, such as described in more detail in preferred embodiments.

[0367] Axes, including time and analyte concentration values, can be
provided on the screen, however are not required. While not wishing to be
bound by theory, it is believed that trend information, thresholds, and
danger zones provide sufficient information to represent analyte
concentration and clinically educate the user. In some embodiments, time
can be represented by symbols, such as a sun and moon to represent day
and night. In some embodiments, the present or most recent measured
analyte concentration, from the continuous sensor and/or from the
reference analyte monitor can be continually, intermittently, or
selectively displayed on the screen.

[0368] The estimated analyte values 448 of FIG. 4B include a portion,
which extends into the danger zone 444. By providing data in a format
that emphasizes the possibility of clinical risk to the patient,
appropriate action can be taken by the user (for example, patient, or
caretaker) and clinical risk can be preempted.

[0369]FIG. 4c is an illustration of the receiver in another embodiment
showing a representation of analyte concentration and directional trend
using a gradient bar. In this embodiment, the screen illustrates the
measured analyte values and estimated analyte values in a simple but
effective manner that communicates valuable analyte information to the
user.

[0370] In this embodiment, a gradient bar 450 is provided that includes
thresholds 452 set at high and lows such as described in more detail with
reference to FIG. 4B, above. Additionally, colors, shading, or other
graphical illustration can be present to represent danger zones 454 on
the gradient bar 450 such as described in more detail with reference to
FIG. 4B, above.

[0371] The measured analyte value is represented on the gradient bar 450
by a marker 456, such as a darkened or colored bar. By representing the
measured analyte value with a bar 456, a low-resolution analyte value is
presented to the user (for example, within a range of values). For
example, each segment on the gradient bar 450 can represent about 10
mg/dL of glucose concentration. As another example, each segment can
dynamically represent the range of values that fall within the "A" and
"B" regions of the Clarke Error Grid. While not wishing to be bound by
theory, it is believed that inaccuracies known both in reference analyte
monitors and/or continuous analyte sensors are likely due to known
variables such as described in more detail elsewhere herein, and can be
de-emphasized such that a user focuses on proactive care of the
condition, rather than inconsequential discrepancies within and between
reference analyte monitors and continuous analyte sensors.

[0372] Additionally, the representative gradient bar communicates the
directional trend of the analyte concentration to the user in a simple
and effective manner, namely by a directional arrow 458. For example, in
conventional diabetic blood glucose monitoring, a person with diabetes
obtains a blood sample and measures the glucose concentration using a
test strip, and the like. Unfortunately, this information does not tell
the person with diabetes whether the blood glucose concentration is
rising or falling. Rising or falling directional trend information can be
particularly important in a situation such as illustrated in FIG. 4c,
wherein if the user does not know that the glucose concentration is
rising, he/she may assume that the glucose concentration is falling and
not attend to his/her condition. However, because rising directional
trend information 458 is provided, the person with diabetes can preempt
the clinical risk by attending to his/her condition (for example,
administer insulin). Estimated analyte data can be incorporated into the
directional trend information by characteristics of the arrow, for
example, size, color, flash speed, and the like.

[0373] In some embodiments, the gradient bar can be a vertical instead of
horizontal bar. In some embodiments, a gradient fill can be used to
represent analyte concentration, variation, or clinical risk, for
example. In some embodiments, the bar graph includes color, for example
the center can be green in the safe zone that graduates to red in the
danger zones; this can be in addition to or in place of the divided
segments. In some embodiments, the segments of the bar graph are clearly
divided by lines; however color, gradation, and the like can be used to
represent areas of the bar graph. In some embodiments, the directional
arrow can be represented by a cascading level of arrows to a represent
slow or rapid rate of change. In some embodiments, the directional arrow
can be flashing to represent movement or pending danger.

[0374] The screen 304 of FIG. 4c can further comprise a numerical
representation of analyte concentration, date, time, or other information
to be communicated to the patient. However, a user can advantageously
extrapolate information helpful for his/her condition using the simple
and effective representation of this embodiment shown in FIG. 4c, without
reading a numeric representation of his/her analyte concentration.

[0375] In some alternative embodiments, a trend graph or gradient bar, a
dial, pie chart, or other visual representation can provide analyte data
using shading, colors, patterns, icons, animation, and the like.

[0376]FIG. 4D is an illustration of a receiver 300 in another embodiment,
including a screen 304 that shows a numerical representation of the most
recent measured analyte value 460. This numerical value 460 is preferably
a calibrated analyte value, such as described in more detail with
reference to FIGS. 5 and 6. Additionally, this embodiment preferably
provides an arrow 462 on the screen 304, which represents the rate of
change of the host's analyte concentration. A bold "up" arrow is shown on
the drawing, which preferably represents a relatively quickly increasing
rate of change. The arrows shown with dotted lines illustrate examples of
other directional arrows (for example, rotated by 45 degrees), which can
be useful on the screen to represent various other positive and negative
rates of change. Although the directional arrows shown have a relative
low resolution (45 degrees of accuracy), other arrows can be rotated with
a high resolution of accuracy (for example one degree of accuracy) to
more accurately represent the rate of change of the host's analyte
concentration (e.g., the amplitude and/or direction of the rate of
change). In some alternative embodiments, the screen provides an
indication of the acceleration of the host's analyte concentration.

[0377] A second numerical value 464 is shown, which is representative of a
variation of the measured analyte value 460. The second numerical value
is preferably determined from a variation analysis based on statistical,
clinical, or physiological parameters, such as described in more detail
elsewhere herein. In one embodiment, the second numerical value 464 is
determined based on clinical risk (for example, weighted for the greatest
possible clinical risk to a patient). In another embodiment, the second
numerical representation 464 is an estimated analyte value extrapolated
to compensate for a time lag, such as described in more detail elsewhere
herein. In some alternative embodiments, the receiver displays a range of
numerical analyte values that best represents the host's estimated
analyte value (for example, +/-10%). In some embodiments, the range is
weighted based on clinical risk to the patient. In some embodiments, the
range is representative of a confidence in the estimated analyte value
and/or a variation of those values. In some embodiments, the range is
adjustable.

[0378] Referring again to FIG. 4A, communication ports, including a PC
communication (com) port 418 and a reference glucose monitor com port 420
can be provided to enable communication with systems that are separate
from, or integral with, the receiver 300. The PC com port 418, for
example, a serial communications port, allows for communicating with
another computer system (e.g., PC, PDA, server, and the like). In one
exemplary embodiment, the receiver 300 is able to download historical
data to a physician's PC for retrospective analysis by the physician. The
reference glucose monitor com port 420 allows for communicating with a
reference glucose monitor (not shown) so that reference glucose values
can be downloaded into the receiver 300, for example, automatically. In
one embodiment, the reference glucose monitor is integral with the
receiver 300, and the reference glucose com port 420 allows internal
communication between the two integral systems. In another embodiment,
the reference glucose monitor com port 420 allows a wireless or wired
connection to reference glucose monitor such as a self-monitoring blood
glucose monitor (e.g., for measuring finger stick blood samples).

Integrated System

[0379] Referring now to FIG. 5, in some embodiments, the receiver 300 is
integrally formed with at least one of a medicament delivery device 502,
and a single point glucose monitor 504. In some embodiments, the receiver
300, medicament delivery device 502 and/or single point glucose monitor
504 are detachably connected, so that one or more of the components can
be individually detached and attached at the user's convenience. In some
embodiments, the receiver 300, medicament delivery device 502, and/or
single point glucose monitor 504 are separate from, detachably
connectable to, or integral with each other; and one or more of the
components are operably connected through a wired or wireless connection,
allowing data transfer and thus integration between the components. In
some embodiments, one or more of the components are operably linked as
described above, while another one or more components (for example, the
syringe or patch) are provided as a physical part of the system for
convenience to the user and as a reminder to enter data for manual
integration of the component with the system. Each of the components of
the integrated system 500 may be manually, semi-automatically, or
automatically integrated with each other, and each component may be in
physical and/or data communication with another component, which may
include wireless connection, wired connection (for example, via cables or
electrical contacts), or the like. Additional description of integrated
systems can be found in U.S. Patent Publication 2005/0192557, entitled
"INTEGRATED DELIVERY DEVICE FOR CONTINUOUS GLUCOSE SENSOR," which is
incorporated herein by reference in its entirety.

[0380] The preferred embodiments provide an integrated system 500, which
includes a medicament delivery device 502 for administering a medicament
to the patient 501. The integrated medicament delivery device can be
designed for bolus injection, continuous injection, inhalation,
transdermal absorption, other method for administering medicament, or any
combinations thereof. The term medicament includes any substance used in
therapy for a patient using the system 500, for example, insulin,
glucagon, or derivatives thereof. Published International Application WO
02/43566 describes glucose, glucagon, and vitamins A, C, or D that may be
used with the preferred embodiments. U.S. Pat. Nos. 6,051,551 and
6,024,090 describe types of insulin suitable for inhalation that may be
used with the preferred embodiments. Patents U.S. Pat. No. 5,234,906,
U.S. Pat. No. 6,319,893, and EP 760677 describe various derivatives of
glucagon that may be used with the preferred embodiments. U.S. Pat. No.
6,653,332 describes a combination therapy that may be used with the
preferred embodiments. U.S. Pat. No. 6,471,689 and WO 81/01794 describe
insulin useful for delivery pumps that may be used with the preferred
embodiments. U.S. Pat. No. 5,226,895 describes a method of providing more
than one type of insulin that may be used with the preferred embodiments.
All of the above references are incorporated herein by reference in their
entirety and may be useful as the medicament(s) in the preferred
embodiments.

[0381] A single point glucose monitor 504 includes a meter for measuring
glucose within a biological sample including a sensing region that has a
sensing membrane impregnated with an enzyme, similar to the sensing
membrane described with reference to U.S. Pat. Nos. 4,994,167 and
4,757,022, which are incorporated herein in their entirety by reference.
However, in alternative embodiments, the single point glucose monitor 504
can use other measurement techniques such as optical, for example. It is
noted that the meter is optional in that a separate meter can be used and
the glucose data downloaded or input by a user into the receiver.

Calibration

[0382] Reference is now made to FIG. 6A, which is a flow chart 600 that
illustrates the process of calibration and data output of the glucose
sensor (e.g., 100A or 100B) in one embodiment.

[0383] Calibration of the glucose sensor comprises data processing that
converts a sensor data stream into an estimated glucose measurement that
is meaningful to a user. In some embodiments, a reference glucose value
can be used to calibrate the data stream from the glucose sensor. In one
embodiment, the analyte sensor is a continuous glucose sensor and one or
more reference glucose values are used to calibrate the data stream from
the sensor. At initialization of a sensor, "initial calibration" is
performed wherein the sensor is initially calibrated. In some
embodiments, during sensor use, "update calibration" is performed to
update the calibration of the sensor. In some embodiments,
"recalibration" is performed to either reinitialize the calibration or
perform an update calibration, for example, when the sensor has
determined that the previous calibration is no longer valid. The
calibration can be performed on a real-time basis and/or retrospectively
recalibrated. However in alternative embodiments, other calibration
techniques can be utilized, for example using another constant analyte
(for example, folic acid, ascorbate, urate, and the like) as a baseline,
factory calibration, periodic clinical calibration, oxygen calibration
(for example, using a plurality of sensor heads), and the like can be
used.

[0384] At block 602, a sensor data receiving module, also referred to as
the sensor data module, or processor module, receives sensor data (e.g.,
a data stream), including one or more time-spaced sensor data points
hereinafter referred to as "data stream," "sensor data," "sensor analyte
data", or "glucose signal," from a sensor via the receiver, which can be
in wired or wireless communication with the sensor. The sensor data can
be raw or smoothed (filtered), or include both raw and smoothed data. In
some embodiments, raw sensor data may include an integrated digital data
value, e.g., a value averaged over a time period such as by a charge
capacitor. Smoothed sensor data point(s) can be filtered in certain
embodiments using a filter, for example, a finite impulse response (FIR)
or infinite impulse response (IIR) filter. Some or all of the sensor data
point(s) can be replaced by estimated signal values to address signal
noise such as described in more detail elsewhere herein. It is noted that
during the initialization of the sensor, prior to initial calibration,
the receiver 300 (e.g., computer system) receives and stores the sensor
data, however it may not display any data to the user until initial
calibration and eventually stabilization of the sensor has been
determined. In some embodiments, the data stream can be evaluated to
determine sensor break-in (equilibrium of the sensor in vitro or in
vivo).

[0385] At block 604, a reference data receiving module, also referred to
as the reference input module, or the processor module, receives
reference data from a reference glucose monitor, including one or more
reference data points. In one embodiment, the reference glucose points
can comprise results from a self-monitored blood glucose test (e.g., from
a finger stick test). In one such embodiment, the user can administer a
self-monitored blood glucose test to obtain a glucose value (e.g., point)
using any known glucose sensor, and enter the numeric glucose value into
the computer system. In another such embodiment, a self-monitored blood
glucose test comprises a wired or wireless connection to the receiver 300
(e.g. computer system) so that the user simply initiates a connection
between the two devices, and the reference glucose data is passed or
downloaded between the self-monitored blood glucose test and the receiver
300. In yet another such embodiment, the self-monitored glucose test is
integral with the receiver 300 so that the user simply provides a blood
sample to the receiver 300, and the receiver 300 runs the glucose test to
determine a reference glucose value. Co-pending U.S. patent application
Ser. No. 10/991,966 filed on Nov. 17, 2004 and entitled "INTEGRATED
RECEIVER FOR CONTINUOUS ANALYTE SENSOR" describes some systems and
methods for integrating a reference analyte monitor into a receiver for a
continuous analyte sensor.

[0386] In some alternative embodiments, the reference data is based on
sensor data from another substantially continuous analyte sensor, e.g., a
transcutaneous analyte sensor or another type of suitable continuous
analyte sensor. In an embodiment employing a series of two or more
transcutaneous (or other continuous) sensors, the sensors can be employed
so that they provide sensor data in discrete or overlapping periods. In
such embodiments, the sensor data from one continuous sensor can be used
to calibrate another continuous sensor, or be used to confirm the
validity of a subsequently employed continuous sensor.

[0387] In some embodiments, the calibration process 600 monitors the
continuous analyte sensor data stream to determine a preferred time for
capturing reference analyte concentration values for calibration of the
continuous sensor data stream. In an example wherein the analyte sensor
is a continuous glucose sensor, when data (for example, observed from the
data stream) changes too rapidly, the reference glucose value may not be
sufficiently reliable for calibration due to unstable glucose changes in
the host. In contrast, when sensor glucose data are relatively stable
(for example, relatively low rate of change), a reference glucose value
can be taken for a reliable calibration. In one embodiment, the
calibration process 600 can prompt the user via the user interface to
"calibrate now" when the analyte sensor is considered stable.

[0388] In some embodiments, the calibration process 600 can prompt the
user via the user interface 416 to obtain a reference analyte value for
calibration at intervals, for example when analyte concentrations are at
high and/or low values. In some additional embodiments, the user
interface 416 can prompt the user to obtain a reference analyte value for
calibration based upon certain events, such as meals, exercise, large
excursions in analyte levels, faulty or interrupted data readings, and
the like. In some embodiments, the estimative algorithms can provide
information useful in determining when to request a reference analyte
value. For example, when estimated analyte values indicate approaching
clinical risk, the user interface 416 can prompt the user to obtain a
reference analyte value.

[0389] Certain acceptability parameters can be set for reference values
received from the user. For example, in one embodiment, the receiver may
only accept reference glucose values between about 40 and about 400
mg/dL.

[0390] In some embodiments, the calibration process 600 performs outlier
detection on the reference data and time corresponding sensor data.
Outlier detection compares a reference analyte value with a time
corresponding measured analyte value to ensure a predetermined
statistically, physiologically, or clinically acceptable correlation
between the corresponding data exists. In an example wherein the analyte
sensor is a glucose sensor, the reference glucose data is matched with
substantially time corresponding calibrated sensor data and the matched
data are plotted on a Clarke Error Grid to determine whether the
reference analyte value is an outlier based on clinical acceptability,
such as described in more detail with reference U.S. Patent Publication
No. US-2005-0027463-A1. In some embodiments, outlier detection compares a
reference analyte value with a corresponding estimated analyte value,
such as described in more detail elsewhere herein and with reference to
the above-described patent application, and the matched data is evaluated
using statistical, clinical, and/or physiological parameters to determine
the acceptability of the matched data pair. In alternative embodiments,
outlier detection can be determined by other clinical, statistical,
and/or physiological boundaries.

[0391] In some embodiments, outlier detection utilizes signal artifacts
detection, described in more detail elsewhere herein, to determine the
reliability of the reference data and/or sensor data responsive to the
results of the signal artifacts detection. For example, if a certain
level of signal artifacts is not detected in the data signal, then the
sensor data is determined to be reliable. As another example, if a
certain level of signal artifacts is detected in the data signal, then
the reference glucose data is determined to be reliable.

[0392] The reference data can be pre-screened according to environmental
and physiological issues, such as time of day, oxygen concentration,
postural effects, and patient-entered environmental data. In one
exemplary embodiment, wherein the sensor comprises an implantable glucose
sensor, an oxygen sensor within the glucose sensor is used to determine
if sufficient oxygen is being provided to successfully complete the
necessary enzyme and electrochemical reactions for accurate glucose
sensing. In another exemplary embodiment, the patient is prompted to
enter data into the user interface, such as meal times and/or amount of
exercise, which can be used to determine likelihood of acceptable
reference data. In yet another exemplary embodiment, the reference data
is matched with time-corresponding sensor data, which is then evaluated
on a modified clinical error grid to determine its clinical
acceptability.

[0393] Some evaluation data, such as described in the paragraph above, can
be used to evaluate an optimum time for reference analyte measurement,
such as described in more detail with reference to FIG. 7.
Correspondingly, the user interface can then prompt the user to provide a
reference data point for calibration within a given time period.
Consequently, because the receiver proactively prompts the user during
optimum calibration times, the likelihood of error due to environmental
and physiological limitations can decrease and consistency and
acceptability of the calibration can increase.

[0394] At block 606, a data matching module, also referred to as the
processor module, matches reference data (e.g., one or more reference
glucose data points) with substantially time corresponding sensor data
(e.g., one or more sensor data points) to provide one or more matched
data pairs. In one embodiment, one reference data point is matched to one
time corresponding sensor data point to form a matched data pair. In
another embodiment, a plurality of reference data points are averaged
(e.g., equally or non-equally weighted average, mean-value, median, and
the like) and matched to one time corresponding sensor data point to form
a matched data pair. In another embodiment, one reference data point is
matched to a plurality of time corresponding sensor data points averaged
to form a matched data pair. In yet another embodiment, a plurality of
reference data points are averaged and matched to a plurality of time
corresponding sensor data points averaged to form a matched data pair.

[0395] In one embodiment, a time corresponding sensor data comprises one
or more sensor data points that occur, for example, 15±5 min after the
reference glucose data timestamp (e.g., the time that the reference
glucose data is obtained). In this embodiment, the 15 minute time delay
has been chosen to account for an approximately 10 minute delay
introduced by the filter used in data smoothing and an approximately 5
minute diffusional time-lag (e.g., the time necessary for the glucose to
diffusion through a membrane(s) of a glucose sensor). In alternative
embodiments, the time corresponding sensor value can be more or less than
in the above-described embodiment, for example ±60 minutes.
Variability in time correspondence of sensor and reference data can be
attributed to, for example, a longer or shorter time delay introduced
during signal estimation, or if the configuration of the glucose sensor
incurs a greater or lesser physiological time lag.

[0396] In another embodiment, time corresponding sensor data comprises one
or more sensor data points that occur from about 0 minutes to about 20
minutes after the reference analyte data time stamp (e.g., the time that
the reference analyte data is obtained). In one embodiment, a 5-minute
time delay is chosen to compensate for a system time-lag (e.g., the time
necessary for the analyte to diffusion through a membrane(s) of an
analyte sensor). In alternative embodiments, the time corresponding
sensor value can be earlier than or later than that of the
above-described embodiment, for example ±60 minutes. Variability in
time correspondence of sensor and reference data can be attributed to,
for example, a longer or shorter time delay introduced by the data
smoothing filter, or if the configuration of the analyte sensor incurs a
greater or lesser physiological time lag.

[0397] In some practical implementations of the sensor, the reference
glucose data can be obtained at a time that is different from the time
that the data is input into the receiver 300. Accordingly, it should be
noted that the "time stamp" of the reference glucose (e.g., the time at
which the reference glucose value was obtained) may not be the same as
the time at which the receiver 300 obtained the reference glucose data.
Therefore, some embodiments include a time stamp requirement that ensures
that the receiver 300 stores the accurate time stamp for each reference
glucose value, that is, the time at which the reference value was
actually obtained from the user.

[0398] In some embodiments, tests are used to evaluate the best-matched
pair using a reference data point against individual sensor values over a
predetermined time period (e.g., about 30 minutes). In one such
embodiment, the reference data point is matched with sensor data points
at 5-minute intervals and each matched pair is evaluated. The matched
pair with the best correlation can be selected as the matched pair for
data processing. In some alternative embodiments, matching a reference
data point with an average of a plurality of sensor data points over a
predetermined time period can be used to form a matched pair.

[0399] In some embodiments wherein the data signal is evaluated for signal
artifacts, as described in more detail elsewhere herein, the processor
module is configured to form a matched data pair only if a signal
artifact is not detected. In some embodiments wherein the data signal is
evaluated for signal artifacts, the processor module is configured to
prompt a user for a reference glucose value during a time when one or
more signal artifact(s) is not detected.

[0400] At block 608, a calibration set module, also referred to as the
processor module, forms an initial calibration set from a set of one or
more matched data pairs, which are used to determine the relationship
between the reference glucose data and the sensor glucose data, such as
described in more detail with reference to block 610, below.

[0401] The matched data pairs, which make up the initial calibration set,
can be selected according to predetermined criteria. In some embodiments,
the number (n) of data pair(s) selected for the initial calibration set
is one. In other embodiments, n data pairs are selected for the initial
calibration set wherein n is a function of the frequency of the received
reference data points. In one exemplary embodiment, six data pairs make
up the initial calibration set. In another embodiment, the calibration
set includes only one data pair. In an embodiment wherein a substantially
continuous analyte sensor provides reference data, numerous data points
are used to provide reference data from more than 6 data pairs (e.g.,
dozens or even hundreds of data pairs). In one exemplary embodiment, a
substantially continuous analyte sensor provides 288 reference data
points per day (every five minutes for twenty-four hours), thereby
providing an opportunity for a matched data pair 288 times per day, for
example. While specific numbers of matched data pairs are referred to in
the preferred embodiments, any suitable number of matched data pairs per
a given time period can be employed.

[0402] In some embodiments, the data pairs are selected only within a
certain glucose value threshold, for example wherein the reference
glucose value is between about 40 and about 400 mg/dL. In some
embodiments, the data pairs that form the initial calibration set are
selected according to their time stamp. In certain embodiments, the data
pairs that form the initial calibration set are selected according to
their time stamp, for example, by waiting a predetermined "break-in" time
period after implantation, the stability of the sensor data can be
increased. In certain embodiments, the data pairs that form the initial
calibration set are spread out over a predetermined time period, for
example, a period of two hours or more. In certain embodiments, the data
pairs that form the initial calibration set are spread out over a
predetermined glucose range, for example, spread out over a range of at
least 90 mg/dL or more.

[0403] In some embodiments, wherein the data signal is evaluated for
signal artifacts, as described in more detail elsewhere herein, the
processor module is configured to utilize the reference data for
calibration of the glucose sensor only if a signal artifact is not
detected.

[0404] At block 610, the conversion function module, also referred to as
the processor module, uses the calibration set to create a conversion
function. The conversion function substantially defines the relationship
between the reference glucose data and the glucose sensor data. A variety
of known methods can be used with the preferred embodiments to create the
conversion function from the calibration set. In one embodiment, wherein
a plurality of matched data points form the initial calibration set, a
linear least squares regression is performed on the initial calibration
set such as described in more detail with reference to FIG. 6B.

[0405] At block 612, a sensor data transformation module, also referred to
as the processor module, uses the conversion function to transform sensor
data into substantially real-time glucose value estimates, also referred
to as calibrated data, or converted sensor data, as sensor data is
continuously (or intermittently) received from the sensor. For example,
the sensor data, which can be provided to the receiver in "counts," is
translated in to estimate analyte value(s) in mg/dL. In other words, the
offset value at any given point in time can be subtracted from the raw
value (e.g., in counts) and divided by the slope to obtain the estimated
glucose value:

mg / dL = ( rawvalue - offset ) slope ##EQU00001##

[0406] In some alternative embodiments, the sensor and/or reference
glucose values are stored in a database for retrospective analysis.

[0407] At block 614, an output module, also referred to as the processor
module, provides output to the user via the user interface. The output is
representative of the estimated glucose value, which is determined by
converting the sensor data into a meaningful glucose value such as
described in more detail with reference to block 612, above. User output
can be in the form of a numeric estimated glucose value, an indication of
directional trend of glucose concentration, and/or a graphical
representation of the estimated glucose data over a period of time, for
example. Other representations of the estimated glucose values are also
possible, for example audio and tactile.

[0408] In one embodiment, such as shown in FIG. 3A, the estimated glucose
value is represented by a numeric value. In other exemplary embodiments,
such as shown in FIGS. 3B to 3D, the user interface graphically
represents the estimated glucose data trend over predetermined a time
period (e.g., one, three, and nine hours, respectively). In alternative
embodiments, other time periods can be represented. In alternative
embodiments, pictures, animation, charts, graphs, ranges of values, and
numeric data can be selectively displayed.

[0409] Accordingly, after initial calibration of the sensor, real-time
continuous glucose information can be displayed on the user interface so
that the user can regularly and proactively care for his/her diabetic
condition within the bounds set by his/her physician.

[0410] In alternative embodiments, the conversion function is used to
predict glucose values at future points in time. These predicted values
can be used to alert the user of upcoming hypoglycemic or hyperglycemic
events. Additionally, predicted values can be used to compensate for a
time lag (e.g., 15 minute time lag such as described elsewhere herein),
if any, so that an estimated glucose value displayed to the user
represents the instant time, rather than a time delayed estimated value.

[0411] In some embodiments, the substantially real-time estimated glucose
value, a predicted future estimated glucose value, a rate of change,
and/or a directional trend of the glucose concentration is used to
control the administration of a constituent to the user, including an
appropriate amount and time, in order to control an aspect of the user's
biological system. One such example is a closed loop glucose sensor and
insulin pump, wherein the glucose data (e.g., estimated glucose value,
rate of change, and/or directional trend) from the glucose sensor is used
to determine the amount of insulin, and time of administration, that can
be given to a diabetic user to evade hyper- and hypoglycemic conditions.

[0412]FIG. 6B is a graph that illustrates one embodiment of a regression
performed on a calibration set to create a conversion function such as
described with reference to FIG. 6A, block 610, above. In this
embodiment, a linear least squares regression is performed on the initial
calibration set. The x-axis represents reference glucose data; the y-axis
represents sensor data. The graph pictorially illustrates regression of
matched pairs 616 in the calibration set. The regression calculates a
slope 618 and an offset 620, for example, using the well-known
slope-intercept equation (y=mx+b), which defines the conversion function.

[0413] In alternative embodiments, other algorithms could be used to
determine the conversion function, for example forms of linear and
non-linear regression, for example fuzzy logic, neural networks,
piece-wise linear regression, polynomial fit, genetic algorithms, and
other pattern recognition and signal estimation techniques.

[0414] In yet other alternative embodiments, the conversion function can
comprise two or more different optimal conversions because an optimal
conversion at any time is dependent on one or more parameters, such as
time of day, calories consumed, exercise, or glucose concentration above
or below a set threshold, for example. In one such exemplary embodiment,
the conversion function is adapted for the estimated glucose
concentration (e.g., high vs. low). For example in an implantable glucose
sensor it has been observed that the cells surrounding the implant will
consume at least a small amount of glucose as it diffuses toward the
glucose sensor. Assuming the cells consume substantially the same amount
of glucose whether the glucose concentration is low or high, this
phenomenon will have a greater effect on the concentration of glucose
during low blood sugar episodes than the effect on the concentration of
glucose during relatively higher blood sugar episodes. Accordingly, the
conversion function can be adapted to compensate for the sensitivity
differences in blood sugar level. In one implementation, the conversion
function comprises two different regression lines, wherein a first
regression line is applied when the estimated blood glucose concentration
is at or below a certain threshold (e.g., 150 mg/dL) and a second
regression line is applied when the estimated blood glucose concentration
is at or above a certain threshold (e.g., 150 mg/dL). In one alternative
implementation, a predetermined pivot of the regression line that forms
the conversion function can be applied when the estimated blood is above
or below a set threshold (e.g., 150 mg/dL), wherein the pivot and
threshold are determined from a retrospective analysis of the performance
of a conversion function and its performance at a range of glucose
concentrations. In another implementation, the regression line that forms
the conversion function is pivoted about a point in order to comply with
clinical acceptability standards (e.g., Clarke Error Grid, Consensus
Grid, mean absolute relative difference, or other clinical cost
function). Although only a few example implementations are described,
other embodiments include numerous implementations wherein the conversion
function is adaptively applied based on one or more parameters that can
affect the sensitivity of the sensor data over time.

[0415] In some other alternative embodiments, the sensor is calibrated
with a single-point through the use of a dual-electrode system to
simplify sensor calibration. In one such dual-electrode system, a first
electrode functions as a hydrogen peroxide sensor including a membrane
system containing glucose-oxidase disposed thereon, which operates as
described herein. A second electrode is a hydrogen peroxide sensor that
is configured similar to the first electrode, but with a modified
membrane system (with the enzyme domain removed, for example). This
second electrode provides a signal composed mostly of the baseline
signal, b.

[0416] In some dual-electrode systems, the baseline signal is
(electronically or digitally) subtracted from the glucose signal to
obtain a glucose signal substantially without baseline. Accordingly,
calibration of the resultant difference signal can be performed by
solving the equation y=mx with a single paired measurement. Calibration
of the implanted sensor in this alternative embodiment can be made less
dependent on the values/range of the paired measurements, less sensitive
to error in manual blood glucose measurements, and can facilitate the
sensor's use as a primary source of glucose information for the user.
Co-pending U.S. patent application Ser. No. 11/004,561 filed Dec. 3, 2004
and entitled, "CALIBRATION TECHNIQUES FOR A CONTINUOUS ANALYTE SENSOR"
describes systems and methods for subtracting the baseline from a sensor
signal.

[0417] In some alternative dual-electrode system embodiments, the analyte
sensor is configured to transmit signals obtained from each electrode
separately (e.g., without subtraction of the baseline signal). In this
way, the receiver can process these signals to determine additional
information about the sensor and/or analyte concentration. For example,
by comparing the signals from the first and second electrodes, changes in
baseline and/or sensitivity can be detected and/or measured and used to
update calibration (e.g., without the use of a reference analyte value).
In one such example, by monitoring the corresponding first and second
signals over time, an amount of signal contributed by baseline can be
measured. In another such example, by comparing fluctuations in the
correlating signals over time, changes in sensitivity can be detected
and/or measured.

[0418] In some alternative embodiments, a regression equation y=mx+b is
used to calculate the conversion function; however, prior information can
be provided for m and/or b, thereby enabling calibration to occur with
fewer paired measurements. In one calibration technique, prior
information (e.g., obtained from in vivo or in vitro tests) determines a
sensitivity of the sensor and/or the baseline signal of the sensor by
analyzing sensor data from measurements taken by the sensor (e.g., prior
to inserting the sensor). For example, if there exists a predictive
relationship between in vitro sensor parameters and in vivo parameters,
then this information can be used by the calibration procedure. For
example, if a predictive relationship exists between in vitro sensitivity
and in vivo sensitivity, m≈f(min vitro), then the predicted
m can be used, along with a single matched pair, to solve for b (b=y-mx).
If, in addition, b can be assumed =0, for example with a dual-electrode
configuration that enables subtraction of the baseline from the signal
such as described above, then both m and b are known a priori, matched
pairs are not needed for calibration, and the sensor can be completely
calibrated e.g. without the need for reference analyte values (e.g.
values obtained after implantation in vivo.)

[0419] In another alternative embodiment, prior information can be
provided to guide or validate the baseline (b) and/or sensitivity (m)
determined from the regression analysis. In this embodiment, boundaries
can be set for the regression line that defines the conversion function
such that working sensors are calibrated accurately and easily (with two
points), and non-working sensors are prevented from being calibrated. If
the boundaries are drawn too tightly, a working sensor may not enter into
calibration. Likewise, if the boundaries are drawn too loosely, the
scheme can result in inaccurate calibration or can permit non-working
sensors to enter into calibration. For example, subsequent to performing
regression, the resulting slope and/or baseline are tested to determine
whether they fall within a predetermined acceptable threshold
(boundaries). These predetermined acceptable boundaries can be obtained
from in vivo or in vitro tests (e.g., by a retrospective analysis of
sensor sensitivities and/or baselines collected from a set of
sensors/patients, assuming that the set is representative of future
data).

[0420] If the slope and/or baseline fall within the predetermined
acceptable boundaries, then the regression is considered acceptable and
processing continues to the next step. Alternatively, if the slope and/or
baseline fall outside the predetermined acceptable boundaries, steps can
be taken to either correct the regression or fail-safe such that a system
will not process or display errant data. This can be useful in situations
wherein regression results in errant slope or baseline values. For
example, when points (matched pairs) used for regression are too close in
value, the resulting regression is statistically less accurate than when
the values are spread farther apart. As another example, a sensor that is
not properly deployed or is damaged during deployment can yield a skewed
or errant baseline signal.

[0421] Reference is now made to FIG. 6c, which is a flow chart 630 that
illustrates the process of immediate calibration of a continuous analyte
sensor in one embodiment.

[0422] In conventional analyte sensors, during initial calibration, update
calibration and/or recalibration of a sensor system, a user must wait a
predetermined period of time (e.g., at least about 5, 10, 15, 20, 25 or
more minutes after entry of a reference analyte value for the system to
output (e.g., display) its first calibrated analyte measurement,
resulting in an inconvenience to the user including a time delayed
response of calibrated sensor data and/or whether or not the reference
analyte value was accepted for calibration.

[0423] The preferred embodiments provide systems and methods to improve
the responsiveness of a user interface (e.g., data output) to received
reference analyte values (e.g., a reading from a blood glucose meter) for
faster feedback to the user. Although calibration preferably compensates
for a time lag between the reference analyte values (e.g., blood glucose
meter readings) and glucose sensor readings (e.g., continuous glucose
sensor readings subject to processing such as filtering), some
circumstances exist wherein an immediate calibration does not compensate
for a time lag (e.g., prior to receiving time-corresponding sensor data).
In one example, a 5-minute time lag is induced in continuous sensor data
by a filter or integrator of raw continuous sensor data (e.g., a
signal)).

[0424] At block 632, a sensor data receiving module, also referred to as
the sensor data module, or processor module, receives sensor data (e.g.,
a data stream), including one or more time-spaced sensor data points
hereinafter referred to as "data stream," "sensor data," "sensor analyte
data", or "glucose signal," from a sensor via the receiver, which can be
in wired or wireless communication with the sensor. The sensor data can
be raw or smoothed (filtered), or include both raw and smoothed data. In
some embodiments, raw sensor data may include an integrated digital data
value, e.g., a value averaged over a time period such as by a charge
capacitor. Smoothed sensor data point(s) can be filtered in certain
embodiments using a filter, for example, a finite impulse response (FIR)
or infinite impulse response (IIR) filter. Some or all of the sensor data
point(s) can be replaced by estimated signal values to address signal
noise such as described in more detail elsewhere herein. It is noted that
during the initialization of the sensor, prior to initial calibration,
the receiver 300 (e.g., computer system) receives and stores the sensor
data, however it may not display any data to the user until initial
calibration and eventually stabilization of the sensor has been
determined. In some embodiments, the data stream can be evaluated to
determine sensor break-in (equilibrium of the sensor in vitro or in
vivo).

[0425] At block 634, a reference data receiving module, also referred to
as the reference input module, or the processor module, receives
reference data from a reference glucose monitor, including one or more
reference data points. In one embodiment, the reference glucose points
can comprise results from a self-monitored blood 504 (e.g., from a finger
stick test). In one such embodiment, the user can administer a
self-monitored blood glucose test to obtain a glucose value (e.g., point)
using any known glucose sensor, and enter the numeric glucose value into
the computer system. In another such embodiment, a self-monitored blood
glucose test comprises a wired or wireless connection to the receiver 300
(e.g. computer system) so that the user simply initiates a connection
between the two devices, and the reference glucose data is passed or
downloaded between the self-monitored blood glucose test and the receiver
300. In yet another such embodiment, the self-monitored glucose test is
integral with the receiver 300 so that the user simply provides a blood
sample to the receiver 300, and the receiver 300 runs the glucose test to
determine a reference glucose value. Co-pending U.S. patent application
Ser. No. 10/991,966 filed on Nov. 17, 2004 and entitled "INTEGRATED
RECEIVER FOR CONTINUOUS ANALYTE SENSOR" describes some systems and
methods for integrating a reference analyte monitor into a receiver for a
continuous analyte sensor.

[0426] In some alternative embodiments, the reference data is based on
sensor data from another substantially continuous analyte sensor, e.g., a
transcutaneous analyte sensor or another type of suitable continuous
analyte sensor. In an embodiment employing a series of two or more
transcutaneous (or other continuous) sensors, the sensors can be employed
so that they provide sensor data in discrete or overlapping periods. In
such embodiments, the sensor data from one continuous sensor can be used
to calibrate another continuous sensor, or be used to confirm the
validity of a subsequently employed continuous sensor.

[0427] At block 636, an data matching is performed by matching a reference
analyte value to the closest sensor data point (e.g., prior, estimated
and/or predicted sensor data point), also referred to as an "immediate
match," such that calibration can be performed and immediate feedback
given to the user. Preferably, immediate calibration enables display of
the one or more estimated analyte values within about 10, 8, 6, 5, 4, 3,
2, or 1 minute(s) of receiving the reference analyte value. Preferably,
immediate calibration is accomplished by matching data pairs immediately,
for example, without compensating for a time lag between the reference
glucose value and the sensor glucose value such that a time stamp of the
reference glucose value is as close as possible to a time stamp of the
sensor glucose value. In some further embodiments, the time stamp of the
reference glucose value is within about 5 minutes, 2.5 minutes, 1 minute
or less of the time stamp of the sensor glucose value in the matched data
pair.

[0428] In another embodiment, an immediate calibration is performed by
matching a reference analyte value to a projected sensor data point
(e.g., using prediction described herein elsewhere) such that calibration
can be performed and immediate feedback given to the user. The projected
value will therefore be used to compensate for the time differential
between obtaining the analyte sensor value and converting this value to
one comparable to the reference analyte value. Preferably, this
embodiment of immediate calibration enables display of the one or more
estimated analyte values within about 10, 8, 6, 5, 4, 3, 2, or 1 minute
of calculating the projected reference analyte value.

[0429] Subsequently, "standard calibration," is performed, also referred
to as subsequent calibration, wherein the reference analyte value can be
re-matched to a more optimal sensor data point, such as described in more
detail elsewhere herein with reference to matching data pairs, for
example, when additional sensor data points are obtained. In some
embodiments, the subsequent calibration, also referred to as "standard
calibration," is performed once additional sensor data is obtained and
matched with the receiving reference analyte value as described in more
detail elsewhere herein, for example with reference to the data matching
module. In some embodiments, the standard calibration utilizes matched
data pairs chosen to adjust for a time lag between a reference glucose
value and a sensor glucose value. In one such example, a time lag is
induced at least in part by a filter applied to raw glucose sensor data
measured by the continuous glucose sensor. In some embodiments, for
example, wherein optimal sensor data for matching with the reference
analyte data is not available (e.g., due to sensor-receiver communication
problems), the immediate calibration is utilized (e.g., calibrated data
displayed using the immediate match) until one or more additional
reference analyte values are available for calibration.

[0430] In some embodiments, immediate calibration provides a calibration
line that determines, predicts or estimates the calibration state that
will be found with the subsequent calibration (e.g., whether the sensor
will be in-calibration or out-of-calibration responsive to the received
reference analyte value). Preferably, the immediate calibration provides
sufficient accuracy such that displayed sensor data during immediate
calibration corresponds to and/or flows with the glucose values displayed
after the standard calibration (e.g., substantially without
non-physiological fluctuations in the displayed data). Accordingly,
immediate calibration is preferably configured with other processing and
fail-safes, as described in more detail elsewhere herein with reference
to calibration (e.g., algorithms such as outlier detection, intelligent
selection of other matched data pairs in the calibration set, and the
like). In preferred embodiments, the conversion function provided by the
immediate calibration (e.g., the immediate match calibration line) is
similar to the conversion function provided by the standard calibration
(e.g., the subsequent calibration line), for example within about +/-20%.

[0431] At block 638, a sensor data transformation module, also referred to
as the processor module, uses a conversion function (described elsewhere
herein) to transform sensor data into substantially real-time glucose
value estimates, also referred to as calibrated data, or converted sensor
data, as sensor data is continuously (or intermittently) received from
the sensor. For example, the sensor data, which can be provided to the
receiver in "counts," is translated in to estimate analyte value(s) in
mg/dL as described in reference to FIG. 6A.

[0432] At block 640, an output module, also referred to as the processor
module, provides output to the user via the user interface. The output is
representative of the estimated glucose value, which is determined by
converting the sensor data into a meaningful glucose value such as
described in more detail with reference to block 612, above. User output
can be in the form of a numeric estimated glucose value, an indication of
directional trend of glucose concentration, and/or a graphical
representation of the estimated glucose data over a period of time, for
example. Other representations of the estimated glucose values are also
possible, for example audio and tactile.

[0433] Reference is now made to FIG. 7, which is a flow chart 700 that
illustrates the process of smart or intelligent calibration of a
continuous analyte sensor in one embodiment. In general, conventional
calibration of analyte sensors can have some inaccuracy, for example,
caused by drift of the sensor signal, algorithmic-induced inaccuracies,
reference analyte measurement error and signal disagreement (e.g.,
between the sensor signal and reference signal). As one example, a
temporary signal disagreement, or a transient lack of correlation between
interstitial analyte sensor data and blood analyte reference data, can be
related to biology, such as differences in interstitial and blood glucose
levels. Accordingly, conventional sensors can suffer from calibration
inaccuracy as a result of temporary signal disagreement, and the like.

[0434] Some conventional continuous analyte sensors request reference data
at predetermined time periods during sensor use (e.g., a sensor session),
for example every 12 hours or at certain predetermined regular or
irregular intervals. However, because of reasons described above, more or
fewer reference data (analyte values) may be required to calibrate the
sensor accurately, which can vary from sensor to sensor and/or host to
host. For example, if a sensor signal exhibits a lot of drift, more
reference data may be necessary. If a sensor signal exhibits very little
drift, less reference data may be sufficient for good sensor calibration.
Preferably, the smart calibration 700 as described herein associates
sensor calibration with sensor performance. Accordingly, the embodiments
described herein enable a sensor that avoids or overcomes calibration
inaccuracies caused by signal drift, temporary signal disagreement, and
the like.

[0435] In some preferred embodiments, systems and methods are provided for
calibration of a continuous glucose sensor, wherein the system determines
an amount of drift on the sensor signal over a time period and requests
reference data when the amount of drift is greater than a threshold.
Sensor drift can be determined by monitoring a change in signal strength
(e.g., using a low pass filter) during a sensor session and/or monitoring
a change in calibration information (e.g., matched data pairs,
calibration set and/or calibration line) over a sensor session, for
example.

[0436] In some preferred embodiments, systems and methods are provided for
calibration of a continuous glucose sensor, wherein the system determines
a predictive accuracy of calibration information and requests reference
data based at least in part on the predictive accuracy of the calibration
information.

[0437] At block 710, a sensor data receiving module, also referred to as
the sensor data module, or processor module, receives sensor data (e.g.,
a data stream), including one or more time-spaced sensor data points
hereinafter referred to as "data stream," "sensor data," "sensor analyte
data", "signal," from a sensor via the receiver, which can be in wired or
wireless communication with the sensor. The sensor data receiving module
is described in more detail elsewhere herein, for example, with reference
to FIG. 5.

[0438] At block 720, a calibration module, also referred to as the
processor module, receives and processes calibration information. In some
embodiments, the calibration module receives reference data from a
reference analyte monitor (e.g., glucose monitor), including one or more
reference data points, which is described in more detail with reference
to the reference data receiving module, for example, with reference to
FIG. 5. In general, reference data can be received at sensor start-up
and/or periodically or intermittently throughout the sensor session. It
is appreciated by one of ordinary skill in the art that reference data
can be received before, during and/or after receiving sensor data.

[0439] In some embodiments, the calibration module, matches reference data
(e.g., one or more reference glucose data points) with substantially time
corresponding sensor data (e.g., one or more sensor data points) to
provide one or more matched data pairs, which is described in more detail
elsewhere herein, for example, with reference to the data matching module
associated with FIG. 6A. In one embodiment, one reference data point is
matched to one time corresponding sensor data point to form a matched
data pair.

[0440] In some embodiments, the calibration module, forms a calibration
set from a set of one or more matched data pairs, which are used to
determine the relationship between the reference analyte (e.g., glucose)
data and the sensor analyte (e.g., glucose data), such as described in
more detail with reference to block 730, for example.

[0441] At block 730, an evaluation module, also referred to as the
processor module, evaluates a predictive accuracy of the calibration
information. The term "predictive accuracy" as used herein is a broad
term and is to be given its ordinary and customary meaning to a person of
ordinary skill in the art (and is not to be limited to a special or
customized meaning), and furthermore refers without limitation to a
measure of how accurate or indicative calibration information is to a
true correlation between the analyte signal and the actual analyte
concentration, for example, a measure of how well a matched data pair, a
plurality of matched data pairs, a calibration set, and/or a calibration
line will accurately predict (i.e., estimate/correlate) glucose
concentration from a sensor signal across a physiologically relevant
range of glucose concentrations (e.g., between about 30 mg/dL and 600
mg/dL of glucose concentration).

[0443] In some embodiments, the evaluation module evaluates a predictive
accuracy by determining a correlation of (or lack of correlation of) one
or more matched pairs (e.g., a newly received matched data pair) with an
existing calibration set. For example, the evaluation module can evaluate
whether a newly received matched data pair(s) fits within the existing
calibration set or the newly received matched data pair(s) changes the
calibration set, such as by evaluating a change in a calibration line
(e.g., regression line) formed with and without the newly received
matched data pair(s) included therein.

[0444] In some embodiments, after receiving a new matched data pair, the
processor module forms a new calibration set that includes the newly
received matched data pair, and forms a new calibration line from the new
calibration set; subsequently, the evaluation module evaluates a
predictive accuracy by evaluating a correlation of the matched data pairs
in the (existing) calibration set (e.g., the calibration set without the
newly received matched data pair) with the new calibration line (e.g.,
formed from the new calibration set including the newly received matched
data pair).

[0445] In some embodiments, after receiving a new matched data pair and
forming a new calibration set including the newly received matched data
pair, the evaluation module evaluates a predictive accuracy by evaluating
a discordance of the new matched data pair and/or the matched data pairs
in the new calibration set. In some embodiments, a new matched pair is
compared against the distribution (e.g., "cloud") of matched data pairs
in the calibration set, whereby a predictive accuracy is determined based
on a correlation and/or deviation of the new matched data pair relative
to the distribution of matched data pairs in the calibration set.

[0446] In some embodiments, the evaluation module evaluates a predictive
accuracy by iteratively evaluating a plurality of combinations of matched
data pairs in the calibration set to obtain a plurality of calibration
lines; for example, if the calibration set includes 5 matched data pairs,
the processor module can systematically remove each of the matched data
pairs from the calibration set, one at a time, and evaluate the resulting
4-data pair calibration sets. One skilled in the art appreciates the
variety of combinations of matched data pairs in a calibration set that
can be evaluated, which is dependent upon the number of matched data
pairs in the calibration set and the number of matched data pairs that
are removed during each iteration, all of which is encompassed herein. In
some embodiments, the processor module removes one or more of the matched
data pairs from the calibration set in response to the iterative
evaluation; for example, due to a lack of correlation of and/or a
discordance of a calibration set and/or calibration line, resulting from
one or more matched data pairs that do not fit well with other of the
matched data pairs in the calibration set. Advantageously, this
embodiment identifies matched data pairs to remove from the calibration
set (e.g., due to inaccuracies and/or drift of the sensor signal).

[0447] In some embodiments, the evaluation module evaluates a predictive
accuracy by evaluating a leverage of the reference data based at least in
part on a glucose concentration associated with the reference data. The
term "leverage" as used herein is a broad term and is to be given its
ordinary and customary meaning to a person of ordinary skill in the art
(and is not to be limited to a special or customized meaning), and
furthermore refers without limitation to a measure of how much
calibration information increases a predictive accuracy of the sensor
calibration, for example, how much newly received reference data
increases the accuracy of the calibration across a physiologically
relevant range of glucose concentration (e.g., 30 to 600 mg/dL). In some
embodiments, the evaluation module evaluates a glucose concentration of
the reference data to determine its leverage in a calibration set,
wherein a glucose concentration that significantly increases the spread
of glucose concentrations represented in a calibration set provides
leverage, and wherein a glucose concentration that does not significantly
increase the spread of glucose concentration represented in the existing
calibration provides more redundancy than leverage, for example.

[0448] In some embodiments, the evaluation module evaluates a predictive
accuracy by evaluating a goodness of fit of a calibration set with a
calibration line drawn from the calibration set. In some embodiments, a
goodness of fit is measured to determine how well the data that form a
regression line actually fit with the regression line, which can be
calculated as an average error (distribution) from the line, and/or a
confidence interval associated with the line drawn from a set of data,
for example.

[0449] In some embodiments, the predictive accuracy is calculated in terms
of a percentage change in baseline value in a dual electrode sensor, for
example, using the percent deviation formula defined as

[(BV1-BV0)/BV0]*100%,

[0450] where, BV1 represents the baseline value at the end of the
period and BV0 represents the value of the baseline at the beginning
of the period. For example, a certain percentage (e.g. 80%, 90% or 100%)
of deviations (e.g., of the baseline value) are within a predefined
deviation range (e.g., no more than 10, 15, 20, 25, 30, 35, 40, 50 or 60
mg/dL) and/or are not more than a predefined percent difference (e.g.,
5%, 10%, 15%, 20%, 25%, 30%, 35%, or 40%). In some embodiments,
predictive accuracy is calculated in terms of a certain deviation value
within certain boundaries (e.g., clinical error grids and/or statistical
thresholds).

[0451] Preferably, the evaluation module evaluates a predictive accuracy
of the calibration information using known statistical and/or clinical
accuracy measures. In some embodiments, the predictive accuracy is
calculated in terms of a percentage, for example a certain percentage of
points within predefined bounds, for example, 60%, 70%, 80%, 85%, 90%,
95%, 98% or 100% of data points with a predefined boundary, such as the A
and B zones of a Clark Error Grid. In some embodiments, the predictive
accuracy is calculated in terms of a difference between the sensor data
and its corresponding reference data, for example, a certain percentage
(e.g., 80%, 90% or 100%) of matched data pairs (e.g., in the calibration
set) are within a predefined glucose concentration range (e.g., no more
than 10, 15, 20, 25, 30, 35, 40, 50 or 60 mg/dL difference) and/or are
not more than a predefined percent difference (e.g., 5%, 10%, 15%, 20%,
25%, 30%, 35%, or 40% percent difference) In some embodiments, a
predictive accuracy is calculated in terms of an R or R-squared value,
for example, when evaluating a correlation or goodness of fit. In some
embodiments, predictive accuracy is calculated in terms of a certain
number of points (or the last point) within certain boundaries (clinical
error grids and/or statistical thresholds). In some embodiments, the
predictive accuracy is calculated on converted sensor data (e.g.,
calibrated data such as glucose concentration in mg/dL). Alternatively,
predictive accuracy is calculated on non-converted/non-calibrated sensor
data.

[0452] At block 740, a determination module, also referred to as the
processor module, determines when to request additional reference data.
In general, the processor module can be programmed to intermittently
request additional reference data at predetermined times during a sensor
session and/or at times determined by the processor module during the
sensor session to increase accuracy of sensor calibration with a minimum
number of reference data requests (e.g., no more than about 2 per day, no
more than about 1 per day, no more than about 7 per 7-day sensor session,
no more than about 5 per 7-day sensor session, no more than about 3 per
7-day sensor session, no more than about 3 per 3-day sensor session and
no more than about 2 per-3 day sensor session). In some embodiments, the
processor module is configured to request additional reference data after
a time period determined in response to the results of the evaluation
described above; this time period can be any time period within the
sensor session, for example, in a sensor configured for 7-days of in vivo
use, the time period is between about 0 minutes and about 7 days. In one
exemplary embodiment, after the evaluation process described with
reference to block 730, the processor module 406 is programmed to request
additional reference data after a time period determined by the
determination module of from about 0-, 5-, 10-, 20-, 30-, 60-minutes, or
2-, 4-, 6-, 9-, 12-, 18-, or 24-hours to about 11/2-, 2-, 3-, 4-, 5-, 6-
or 7-days.

[0453] Accordingly, in some embodiments, the computer system (e.g., the
processor module) is configured to request reference data at a time
determined by the evaluation of the matched data pair and/or the
calibration set. In one exemplary embodiment, the computer system is
configured to display an amount of time before a next reference data will
be requested.

[0454] In some embodiments, for example, when the evaluation module
evaluates a correlation of the new matched data pair with the calibration
set, the determination module determines when to request additional
reference data based at least in part on the correlation of the new
matched data pair and the calibration set.

[0455] In some embodiments, for example, when the evaluation module 730
evaluates a correlation of the matched data pairs in an (existing)
calibration set (e.g., a calibration set without a newly received matched
data pair) with a new calibration line (e.g., formed from a new
calibration set including the newly received matched data pair), the
determination module 740 determines when to request additional reference
data based at least in part on the correlation of the matched pairs in
the calibration set and the new calibration line.

[0456] In some embodiments, for example, when the evaluation module 730
evaluates a discordance of the new matched data pair and/or the matched
data pairs in the new calibration set, the determination module
determines when to request additional reference data based at least in
part on the discordance of the new matched data pair and/or the matched
data pairs in the new calibration set.

[0457] In some embodiments, for example, wherein the evaluation module 730
iteratively evaluates a plurality of combinations of matched data pairs
in the calibration set to obtain a plurality of calibration lines, the
determination module 740 determines when to request additional reference
data based at least in part on the iterative evaluation.

[0458] In some embodiments, for example, wherein the evaluation module 730
evaluates an accuracy of the calibration set, the determination module
740 determines when to request additional reference data based at least
in part on the accuracy of the calibration line and an estimated glucose
concentration.

[0459] Accordingly, the predictive accuracy of the calibration information
such as described above, can be quantified by the computer system (e.g.,
processor module) and a time to next reference value determined (e.g., in
order to determine when the next reference value should be requested). In
general, the results of the predictive accuracy are input into a model
and a time to next reference data determined, wherein a greater
predictive accuracy results in a longer time to next reference data
request and a lesser predictive accuracy results in a shorter time to
next reference data, which allows the number of reference data requests
to be minimized while ensuring a level of predictive accuracy in the
sensor calibration. In some embodiments, the model includes a look-up
table, wherein the results of the predictive accuracy are compared
against a table, and a time to next reference data request determined. In
some embodiments, the results of the predictive accuracy are input into a
formula, function or equation, and the time to next reference data
request determined.

[0460] In some embodiments, the predictive accuracy is calculated and
compared against one or more thresholds (e.g., 1, 2, 3, 4, 5, 6, 7 or
more thresholds or criteria), from which an output is determined. In one
exemplary embodiment, if the predictive accuracy is determined to be
within a first range of accuracy (e.g., at least about 70%, 75%, 80%,
85%, 90%, 95%), then additional reference data is/are not requested by
the processor module for (or is requested by the processor module after)
a first request time period (e.g., 12, 24, 36, or 48 hours), wherein the
first request time period is longer than other request time periods
(described below). If the predictive accuracy is within a second range of
accuracy (e.g., at least about 30%, 35%, 40%, 45%, 50%, 55%, 60%, or 65%,
and/or no more than about 65%, 70%, 75%, 80%, 85%, or 90%), then
additional reference data is/are not requested by the processor module
for (or is requested by the processor module after) a second request time
period (e.g., 3 hours, 6 hours, 9 hours, 12 hours, or 18 hours), wherein
the second request time period is less than the first time period. If the
predictive accuracy is within a third range of accuracy (e.g., no more
than about 30%, 35%, 40%, 45%, 50%, 55%, 60%, or 65%), then additional
reference data is/are not requested by the processor module for (or is
requested by the processor module after) a third request time period
(e.g., 0 minutes, 30 minutes, 1 hour, 2 hours, 3 hours, 6 hours, or 9
hours), wherein the third request time period is less than the second
time period. Although one exemplary embodiment with three ranges of
accuracy is described above, one, two, three, four, five or more ranges,
thresholds, criteria, and the like, can be used to determine when to
request additional reference data.

[0461] In some embodiments, wherein the evaluation module evaluates the
glucose concentration(s) associated with the matched data pair(s) in the
calibration set, the processor module is configured to request reference
data based on present glucose concentration and glucose concentration
associated with matched pairs in the cal set, for example, the
determination module 740 is configured to request additional reference
data when the host's glucose concentration is at a level that would
increase the spread of the glucose concentrations associated with the
matched data pairs in the calibration set. Advantageously, an increased
spread of matched data pairs in a calibration set increases accuracy of
the calibration line.

[0462] In some embodiments, one or more of the above embodiments are
combined; for example, the determination module 740 can be configured to
determine when to request additional reference data based on accuracy of
the matched data pairs in the calibration set and a spread of glucose
concentrations associated with matched data pairs in the calibration set.

[0463] In some embodiments, the dual electrode sensor (as described
elsewhere herein) can be configured to provide information that can be
used to evaluate a predictive accuracy. In an exemplary embodiment, the
dual-electrode analyte sensor includes a non-enzyme or second working
electrode that is configured to generate a signal associated with
background noise. Certain fluctuations in the non-enzyme related signal
can be indicators of drift and can be quantified to give a predictive
accuracy, which can provide an indication of when calibration is needed,
for example.

[0464] In an exemplary embodiment, a regression analysis is performed to
calibrate information using the slope-intercept equation (y=mx+b), which
defines a conversion function (described elsewhere herein), where the
value of the baseline (b) is represented by the signal associated with
the background noise. A predictive accuracy can be calculated using this
equation by analyzing a deviation in the value of b over a period of time
(e.g. 1 min, 5 min, 10 min, 1 hour, 12 hours, 24 hours, 36 hours, 48
hours, or more).

[0465] Additionally or alternatively to the determination module, the
predictive accuracy determined by the evaluation module enables decision
making of display, calibration, alarming, sensor health/diagnostics,
insulin delivery, and the like. In some embodiments, the output module,
or processor module, is configured to control an output based at least in
part on the predictive accuracy. In some embodiments, the system is
configured to control a display (e.g., a user interface 416) based at
least in part on a predictive accuracy. In some embodiments, the system
is configured to control the display of raw and/or filtered data (e.g.,
on a user interface or display) based at least in part on a predictive
accuracy. In some embodiments, the system is configured to display rate
of change information based at least in part on a predictive accuracy. In
some embodiments, the system is configured to control alarms indicative
of at least one of hypoglycemia, hyperglycemia, predicted hypoglycemia,
and predicted hyperglycemia based at least in part on a predictive
accuracy. In some embodiments, the system is configured to controlling
insulin delivery and/or insulin therapy instructions based at least in
part on a predictive accuracy, for example, when to fall back to a more
conservative recommendation or when to open the loop (request user
interaction) of a closed loop insulin delivery system. In some
embodiments, the system is configured to diagnose a sensor condition
based at least in part on a predictive accuracy. In some embodiments, the
system is configured to suspend display of sensor data based at least in
part on a predictive accuracy. In some embodiments, the system is
configured to shut down a sensor session based at least in part on a
predictive accuracy.

[0466] Additional methods for processing sensor glucose data are disclosed
in U.S. Patent Publication No. US-2005-0027463-A1. In view of the
above-described data processing, it is believed that improving the
accuracy of the data stream will be advantageous for improving output of
glucose sensor data. Accordingly, the following description is related to
improving data output by decreasing signal artifacts on the raw data
stream from the sensor. The data smoothing methods of preferred
embodiments can be employed in conjunction with any sensor or monitor
measuring levels of an analyte in vivo, wherein the level of the analyte
fluctuates over time, including but not limited to such sensors as
described in U.S. Pat. No. 6,001,067 to Shults et al.; U.S. Patent
Publication No. US-2003-0023317-A1 U.S. Pat. No. 6,212,416 to Ward et
al.; U.S. Pat. No. 6,119,028 to Schulman et al; U.S. Pat. No. 6,400,974
to Lesho; U.S. Pat. No. 6,595,919 to Berner et al.; U.S. Pat. No.
6,141,573 to Kurnik et al.; U.S. Pat. No. 6,122,536 to Sun et al.;
European Patent Application EP 1153571 to Varall et al.; U.S. Pat. No.
6,512,939 to Colvin et al.; U.S. Pat. No. 5,605,152 to Slate et al.; U.S.
Pat. No. 4,431,004 to Bessman et al.; U.S. Pat. No. 4,703,756 to Gough et
al; U.S. Pat. No. 6,514,718 to Heller et al; and U.S. Pat. No. 5,985,129
to Gough et al.

Signal

[0467] Generally, implantable sensors measure a signal related to an
analyte of interest in a host. For example, an electrochemical sensor can
measure glucose, creatinine, or urea in a host, such as an animal (e.g.,
a human). Generally, the signal is converted mathematically to a numeric
value indicative of analyte status, such as analyte concentration, such
as described in more detail, above. The signal detected by the sensor can
be broken down into its component parts. For example, in an enzymatic
electrochemical analyte sensor, preferably after sensor break-in is
complete, the total signal can be divided into an "analyte component,"
which is representative of analyte (e.g., glucose) concentration, and a
"noise component," which is caused by non-analyte-related species that
have a redox potential that substantially overlaps with the redox
potential of the analyte (or measured species, e.g., H2O2) at
an applied voltage. The noise component can be further divided into its
component parts, i.e., constant and non-constant noise. It is not unusual
for a sensor to experience a certain level of noise. In general,
"constant noise" (sometimes referred to as constant background or
baseline) is caused by non-analyte-related factors that are relatively
stable over time, including but not limited to electroactive species that
arise from generally constant (e.g., daily) metabolic processes. Constant
noise can vary widely between hosts. In contrast, "non-constant noise"
(sometimes referred to as non-constant background, signal artifacts,
signal artifact events (or episodes), transient noise, noise events,
noise episodes, and the like) is caused by non-constant,
non-analyte-related species (e.g., non-constant noise-causing
electroactive species) that arise during transient events, such as during
host metabolic processes (e.g., wound healing or in response to an
illness), or due to ingestion of certain compounds (e.g., certain drugs).
In some circumstances, noise can be caused by a variety of noise-causing
electroactive species, which are discussed in detail elsewhere herein.

[0468]FIG. 8A is a graph illustrating the components of a signal measured
by a transcutaneous glucose sensor (after sensor break-in was complete),
in a non-diabetic volunteer host. The Y-axis indicates the signal
amplitude (in counts) detected by the sensor. The term "counts" as used
herein is a broad term, and is to be given its ordinary and customary
meaning to a person of ordinary skill in the art (and it is not to be
limited to a special or customized meaning), and refers without
limitation to a unit of measurement of a digital signal. In one example,
a raw data stream measured in counts is directly related to a voltage
(for example, converted by an A/D converter), which is directly related
to current from a working electrode. The X-axis indicates time.

[0469] The total signal collected by the sensor is represented by line
800, which includes components related to glucose, constant noise, and
non-constant noise, which are described in more detail elsewhere herein.
In some embodiments, the total signal is a raw data stream, which can
include a signal averaged or integrated by a charge-counting device, for
example.

[0470] The non-constant noise component of the total signal is represented
by line 802. The non-constant noise component 802 of the total signal 800
can be obtained by filtering the total signal 800 to obtain a filtered
signal 804 using any of a variety of known filtering techniques, and then
subtracting the filtered signal 804 from the total signal 800. In some
embodiments, the total signal can be filtered using linear regression
analysis of the n (e.g., 10) most recent sampled sensor values. In some
embodiments, the total signal can be filtered using non-linear
regression. In some embodiments, the total signal can be filtered using a
trimmed regression, which is a linear regression of a trimmed mean (e.g.,
after rejecting wide excursions of any point from the regression line).
In this embodiment, after the sensor records glucose measurements at a
predetermined sampling rate (e.g., every 30 seconds), the sensor
calculates a trimmed mean (e.g., removes highest and lowest measurements
from a data set) and then regresses the remaining measurements to
estimate the glucose value. In some embodiments, the total signal can be
filtered using a non-recursive filter, such as a finite impulse response
(FIR) filter. An FIR filter is a digital signal filter, in which every
sample of output is the weighted sum of past and current samples of
input, using only some finite number of past samples. In some
embodiments, the total signal can be filtered using a recursive filter,
such as an infinite impulse response (IIR) filter. An IIR filter is a
type of digital signal filter, in which every sample of output is the
weighted sum of past and current samples of input. In some embodiments,
the total signal can be filtered using a maximum-average (max-average)
filtering algorithm, which smoothes data based on the discovery that the
substantial majority of signal artifacts observed after implantation of
glucose sensors in humans, for example, is not distributed evenly above
and below the actual blood glucose levels. It has been observed that many
data sets are actually characterized by extended periods in which the
noise appears to trend downwardly from maximum values with occasional
high spikes. To overcome these downward trending signal artifacts, the
max-average calculation tracks with the highest sensor values, and
discards the bulk of the lower values. Additionally, the max-average
method is designed to reduce the contamination of the data with
unphysiologically high data from the high spikes. The max-average
calculation smoothes data at a sampling interval (e.g., every 30 seconds)
for transmission to the receiver at a less frequent transmission interval
(e.g., every 5 minutes), to minimize the effects of low non-physiological
data. First, the processor finds and stores a maximum sensor counts value
in a first set of sampled data points (e.g., 5 consecutive, accepted,
thirty-second data points). A frame shift time window finds a maximum
sensor counts value for each set of sampled data (e.g., each 5-point
cycle length) and stores each maximum value. The processor then computes
a rolling average (e.g., 5-point average) of these maxima for each
sampling interval (e.g., every 30 seconds) and stores these data.
Periodically (e.g., every 10th interval), the sensor outputs to the
receiver the current maximum of the rolling average (e.g., over the last
10 thirty-second intervals as a smoothed value for that time period
(e.g., 5 minutes)). In some embodiments, the total signal can be filtered
using a "Cone of Possibility Replacement Method," which utilizes
physiological information along with glucose signal values in order
define a "cone" of physiologically feasible glucose signal values within
a human. Particularly, physiological information depends upon the
physiological parameters obtained from continuous studies in the
literature as well as our own observations. A first physiological
parameter uses a maximal sustained rate of change of glucose in humans
(e.g., about 4 to 5 mg/di/min) and a maximum sustained acceleration of
that rate of change (e.g., about 0.1 to 0.2 mg/min/min). A second
physiological parameter uses the knowledge that rate of change of glucose
is lowest at the maxima and minima, which are the areas of greatest risk
in patient treatment. A third physiological parameter uses the fact that
the best solution for the shape of the curve at any point along the curve
over a certain time period (e.g., about 20-25 minutes) is a straight
line. It is noted that the maximum rate of change can be narrowed in some
instances. Therefore, additional physiological data can be used to modify
the limits imposed upon the Cone of Possibility Replacement Method for
sensor glucose values. For example, the maximum per minute rate change
can be lower when the subject is lying down or sleeping; on the other
hand, the maximum per minute rate change can be higher when the subject
is exercising, for example. In some embodiments, the total signal can be
filtered using reference changes in electrode potential to estimate
glucose sensor data during positive detection of signal artifacts from an
electrochemical glucose sensor, the method hereinafter referred to as
reference drift replacement. In this embodiment, the electrochemical
glucose sensor comprises working, counter, and reference electrodes. This
method exploits the function of the reference electrode as it drifts to
compensate for counter electrode limitations during oxygen deficits, pH
changes, and/or temperature changes. In alternative implementations of
the reference drift method, a variety of algorithms can therefore be
implemented based on the changes measured in the reference electrode.
Linear algorithms, and the like, are suitable for interpreting the direct
relationship between reference electrode drift and the non-glucose rate
limiting signal noise such that appropriate conversion to signal noise
compensation can be derived. Additional description of signal filtering
can be found in more detail elsewhere herein.

[0471] Referring again to FIG. 8A, the constant noise signal component 806
can be obtained by calibrating the sensor signal using reference data,
such as one or more blood glucose values obtained from a hand-held blood
glucose meter, from which the baseline "b" of a regression can be
obtained, representing the constant noise signal component 806.

[0472] The analyte signal component 808 can be obtained by subtracting the
constant noise signal component 806 from the filtered signal 804.

Noise

[0473] Noise is clinically important because it can induce error and can
reduce sensor performance, such as by providing a signal that causes the
analyte concentration to appear higher or lower than the actual analyte
concentration. For example, upward or high noise (e.g., noise that causes
the signal to increase) can cause the host's glucose concentration to
appear higher than it truly is which can lead to improper treatment
decisions. Similarly, downward or low noise (e.g., noise that causes the
signal to decrease) can cause the host's glucose concentration to appear
lower than it is which can also lead to improper treatment decisions.

[0474] Noise can be caused by a variety of factors, ranging from
mechanical factors to biological factors. For example, it is known that
macro- or micro-motion, ischemia, pH changes, temperature changes,
pressure, stress, or even unknown mechanical, electrical, and/or
biochemical sources can cause noise, in some embodiments. Interfering
species, which are known to cause non-constant noise, can be compounds,
such as drugs that have been administered to the host, or intermittently
produced products of various host metabolic processes. Exemplary
interferents include but are not limited to a variety of drugs (e.g.,
acetaminophen), H2O2 from exterior sources (e.g., produced
outside the sensor membrane system), and reactive metabolic species
(e.g., reactive oxygen and nitrogen species, some hormones, etc.). Some
known interfering species for a glucose sensor include but are not
limited to acetaminophen, ascorbic acid, bilirubin, cholesterol,
creatinine, dopamine, ephedrine, ibuprofen, L-dopa, methyldopa,
salicylate, tetracycline, tolazamide, tolbutamide, triglycerides, and
uric acid.

[0475] In some experiments of implantable glucose sensors, it was observed
that noise increased when some hosts were intermittently sedentary, such
as during sleep or sitting for extended periods. When the host began
moving again, the noise quickly dissipated. Noise that occurs during
intermittent, sedentary periods (sometimes referred to as intermittent
sedentary noise) can occur during relatively inactive periods, such as
sleeping. Non-constant, non-analyte-related factors can cause
intermittent sedentary noise, such as was observed in one exemplary study
of non-diabetic individuals implanted with enzymatic-type glucose sensors
built without enzyme. These sensors (without enzyme) could not react with
or measure glucose and therefore provided a signal due to non-glucose
effects only (e.g., constant and non-constant noise). During sedentary
periods (e.g., during sleep), extensive, sustained signal was observed on
the sensors. Then, when the host got up and moved around, the signal
rapidly corrected. As a control, in vitro experiments were conducted to
determine if a sensor component might have leached into the area
surrounding the sensor and caused the noise, but none was detected. From
these results, it is believed that a host-produced non-analyte related
reactant was diffusing to the electrodes and producing the unexpected
non-constant noise signal.

[0476] While not wishing to be bound by theory, it is believed that a
concentration increase of noise-causing electroactive species, such as
electroactive metabolites from cellular metabolism and wound healing, can
interfere with sensor function and cause noise observed during host
sedentary periods. For example, local lymph pooling, which can occur when
a part of the body is compressed or when the body is inactive, can cause,
in part, this local build up of interferants (e.g., electroactive
metabolites). Similarly, a local accumulation of wound healing metabolic
products (e.g., at the site of sensor insertion) likely causes noise on
the sensor. Noise-causing electroactive species can include but are not
limited to compounds with electroactive acidic, amine or sulfhydryl
groups, urea, lactic acid, phosphates, citrates, peroxides, amino acids
(e.g., L-arginine), amino acid precursors or break-down products, nitric
oxide (NO), NO-donors, NO-precursors or other electroactive species or
metabolites produced during cell metabolism and/or wound healing, for
example. For a more complete discussion of noise and its sources, see
U.S. Patent Publication No. US-2007-0027370-A1.

[0477] Noise can be recognized and/or analyzed in a variety of ways. For
example, in some circumstances, non-constant noise changes faster than
the analyte signal and/or does not follow an expected analyte signal
pattern; and lasts for a period of about 10 hours or more, 8 hours, 6
hours, 4 hours, 2 hours, 60 minutes, 30 minutes, or 10 minutes or less.
In some embodiments, the sensor data stream can be monitored, signal
artifacts detected, and data processing performed based at least in part
on whether or not a signal artifact has been detected, such as described
in more detail elsewhere herein.

[0478] In some conventional analyte sensors, non-constant noise can be a
significant component of the total signal, for example, 30%, 40%, 50%,
60% or more of the total signal. Additionally, non-constant noise can
occur for durations of minutes or hours, in some circumstances. In some
circumstances, non-constant noise can be equivalent to a glucose
concentration of about 400-mg/dl or more. Noise can induce error in the
sensor reading, which can reduce sensor accuracy and clinically useful
data. However, a high level of sensor accuracy is critical for successful
patient care and desirable clinical outcomes.

[0479] In some embodiments, an electrochemical analyte detection system is
provided, which includes a sensor configured for substantially continuous
analyte detection, such as in an ambulatory host. The sensor includes at
least one electrode and electronics configured to provide a signal
measured at the electrode; wherein the measured signal can be broken down
(e.g., after sensor break-in) into its component parts, a substantially
analyte-related component, a substantially constant non-analyte-related
component (i.e., constant noise) and a substantially non-constant
non-analyte-related component (i.e., non-constant noise).

[0480] In some embodiments, a signal component's percentage of the total
signal is determined using one or more of a variety of methods of
quantifying an amplitude of signal components and total signal, from
which each components percent contribution can be calculated, as is
appreciated by one skilled in the art. In some embodiments, the signal
component(s) can be quantified by comparing the peak-to-peak amplitudes
of each signal component for a time period, whereby the peak-to-peak
amplitudes of each component can be compared to the peak-to-peak
amplitude of the total signal to determine its percentage of the total
signal, as is appreciated by one skilled in the art. In some embodiments,
the signal component(s) can be quantified by determining the Root Mean
Square (RMS) of the signal component for a time period. In one exemplary
of Root Mean Square analysis of signal components, the signal
component(s) can be quantified using the formula:

RMS = ( x 1 2 + x 2 2 + x 3 2 + x n 2 ) n
##EQU00002##

wherein there are a number (n) of data values (x) for a signal (e.g.,
analyte component, non-constant noise component, constant noise
component, and/or total signal) during a predetermined time period (e.g.,
about 1 day, about 2 days, about 3 days, etc). Once the signal components
and/or total signal are quantified, the signal components can be compared
to the total signal to determine a percentage of each signal component
within the total signal.

Signal Artifact Detection and Replacement

[0481] Typically, a glucose sensor produces a data stream that is
indicative of the glucose concentration of a host, such as described in
more detail above. However, it is well known that of the glucose sensors
described above, there are only a few examples of glucose sensors that
are able to provide a raw data output indicative of the concentration of
glucose. Thus, it should be understood that the systems and methods
described herein, including signal artifacts detection, signal artifacts
replacement, and other data processing, can be applied to a data stream
obtained from any glucose sensor.

[0482] Raw data streams typically have some amount of "system noise,"
caused by unwanted electronic or diffusion-related noise that degrades
the quality of the signal and thus the data. Accordingly, conventional
glucose sensors are known to smooth raw data using methods that filter
out this system noise, and the like, in order to improve the signal to
noise ratio, and thus data output. One example of a conventional
data-smoothing algorithm includes a finite impulse response filter (FIR),
which is particularly suited for reducing high-frequency noise (see Steil
et al. U.S. Pat. No. 6,558,351).

[0483] FIGS. 8B and 8C are graphs of raw data streams from an implantable
glucose sensor prior to data smoothing in one embodiment. FIG. 8B is a
graph that shows a raw data stream obtained from a glucose sensor over an
approximately 4 hour time span in one example. FIG. 8c is a graph that
shows a raw data stream obtained from a glucose sensor over an
approximately 36 hour time span in another example. The x-axis represents
time in minutes. The y-axis represents sensor data in counts. In these
examples, sensor output in counts is transmitted every 30-seconds.

[0484] The "system noise" such as shown in sections 810a, 810b of the data
streams of FIGS. 8B and 8C, respectively, illustrate time periods during
which system noise can be seen on the data stream. This system noise can
be characterized as Gaussian, Brownian, and/or linear noise, and can be
substantially normally distributed about the mean. The system noise is
likely electronic and diffusion-related, and the like, and can be
smoothed using techniques such as by using an FIR filter. As another
example, the raw data can be represented by an integrated value, for
example, by integrating the signal over a time period (e.g., 30 seconds
or 5 minutes), and providing an averaged (e.g., integrated) data point
there from. The system noise such as shown in the data of sections 810a,
810b is a fairly accurate representation of glucose concentration and can
be confidently used to report glucose concentration to the user when
appropriately calibrated.

[0485] The "signal artifacts," also referred to as "signal artifact
events" or "noise episodes" for example, such as shown in sections 812a,
812b of the data stream of FIGS. 8B and 8C, respectively, illustrate time
periods during which "signal artifacts" can be seen, which are
significantly different from the previously described system noise
(sections 810a, 810b). This noise, such as shown in section 812a and
812b, is referred to herein as "signal artifacts" and may be described as
"transient non-glucose dependent signal artifacts that have higher
amplitude than system noise." At times, signal artifacts comprise low
noise, which generally refers to noise that substantially decreases
signal amplitude 814a, 814b herein, which is best seen in the signal
artifacts 812b of FIG. 8c. Occasional high spikes 816a, 816b, which
generally correspond to noise that substantially increases signal
amplitude, can also be seen in the signal artifacts, which generally
occur after a period of low noise. These high spikes are generally
observed after transient low noise and typically result after reaction
rate-limiting phenomena occur. For example, in an embodiment where a
glucose sensor requires an enzymatic reaction, local ischemia creates a
reaction that is rate-limited by oxygen, which is responsible for low
noise. In this situation, glucose would be expected to build up in the
membrane because it would not be completely catabolized during the oxygen
deficit. When oxygen is again in excess, there would also be excess
glucose due to the transient oxygen deficit. The enzyme rate would speed
up for a short period until the excess glucose is catabolized, resulting
in high noise. Additionally, noise can be distributed both above and
below the expected signal.

[0486] Analysis of signal artifacts such as shown sections 812a, 812b of
FIGS. 8B and 8C, respectively, indicates that the observed low noise is
caused by substantially non-glucose reaction dependent phenomena, such as
ischemia that occurs within or around a glucose sensor in vivo, for
example, which results in the reaction becoming oxygen dependent. As a
first example, at high glucose levels, oxygen can become limiting to the
enzymatic reaction, resulting in a non-glucose dependent downward trend
in the data (best seen in FIG. 8c). As a second example, certain
movements or postures taken by the patient can cause transient downward
noise as blood is squeezed out of the capillaries resulting in local
ischemia, and causing non-glucose dependent low noise. Because excess
oxygen (relative to glucose) is necessary for proper sensor function,
transient ischemia can result in a loss of signal gain in the sensor
data. In this second example oxygen can also become transiently limited
due to contracture of tissues around the sensor interface. This is
similar to the blanching of skin that can be observed when one puts
pressure on it. Under such pressure, transient ischemia can occur in both
the epidermis and subcutaneous tissue. Transient ischemia is common and
well tolerated by subcutaneous tissue.

[0487] In another example of non-glucose reaction rate-limiting phenomena,
skin temperature can vary dramatically, which can result in thermally
related erosion of the signal (e.g., temperature changes between 32 and
39 degrees Celsius have been measured in humans). In yet another
embodiment, wherein the glucose sensor is placed intravenously, increased
impedance can result from the sensor resting against wall of the blood
vessel, for example, producing this non-glucose reaction rate-limiting
noise due to oxygen deficiency.

[0488]FIG. 9 is a flow chart 900 that illustrates the process of
detecting and replacing signal artifacts in certain embodiments. It is
noted that "signal artifacts" particularly refers to the transient
non-glucose related artifacts such as described in more detail elsewhere
herein. Typically, signal artifacts are caused by non-glucose
rate-limiting phenomenon such as described in more detail above.

[0489] At block 910, a sensor data receiving module, also referred to as
the sensor data module 910, or processor module, receives sensor data
(e.g., a data stream), including one or more time-spaced sensor data
points. In some embodiments, the data stream is stored in the sensor for
additional processing; in some alternative embodiments, the sensor
periodically transmits the data stream to the receiver 300, which can be
in wired or wireless communication with the sensor. In some embodiments,
raw and/or filtered data is stored in the sensor and/or receiver.

[0490] At block 912, a signal artifacts detection module, also referred to
as the signal artifacts detector 914 or signal reliability module, is
programmed to detect transient non-glucose related signal artifacts in
the data stream. The signal artifacts detector can comprise an oxygen
detector, a pH detector, a temperature detector, and/or a pressure/stress
detector, for example, the signal artifacts detector 228 in FIG. 2. In
some embodiments, the signal artifacts detector at block 912 is located
within the processor 214 in FIG. 2 and utilizes existing components of
the glucose sensor to detect signal artifacts, for example by pulsed
amperometric detection, counter electrode monitoring, reference electrode
monitoring, and frequency content monitoring, which are described
elsewhere herein. In yet other embodiments, the data stream can be sent
from the sensor to the receiver which comprises programming in the
processor 406 in FIG. 4A that performs algorithms to detect signal
artifacts, for example such as described with reference to "Cone of
Possibility Detection" method and/or by comparing raw data vs. filtered
data, both of which are described in more detail below. Numerous
embodiments for detecting signal artifacts are described in more detail
in the section entitled, "Signal Artifacts Detection and Replacement,"
all of which are encompassed by the signal artifacts detection at block
912.

[0491] In certain embodiments, the processor module in either the sensor
electronics and/or the receiver electronics can evaluate an intermittent
or continuous signal-to-noise measurement to determine aberrancy of
sensor data responsive to a signal-to-noise ratio above a set threshold.
In certain embodiments, signal residuals (e.g., by comparing raw and
filtered data) can be intermittently or continuously analyzed for noise
above a set threshold. In certain embodiments, pattern recognition can be
used to identify noise associated with physiological conditions, such as
low oxygen, or other known signal aberrancies. Accordingly, in these
embodiments, the system can be configured, in response to aberrancies in
the data stream, to trigger signal estimation, adaptively filter the data
stream according to the aberrancy, and the like, as described in more
detail elsewhere herein.

[0492] At block 914, the signal artifacts replacement module, also
referred to as the signal estimation module, replaces some or an entire
data stream with estimated glucose signal values using signal estimation.
Numerous embodiments for performing signal estimation are described in
more detail in the section entitled "Signal Artifacts Detection and
Replacement," all of which are encompassed by the signal artifacts
replacement module, block 914. It is noted that in some embodiments,
signal estimation/replacement is initiated in response to positive
detection of signal artifacts on the data stream, and subsequently
stopped in response to detection of negligible signal artifacts on the
data stream. In some embodiments, the system waits a predetermined time
period (e.g., between 30 seconds and 30 minutes) before switching the
signal estimation on or off to ensure that a consistent detection has
been ascertained. In some embodiments, however, signal
estimation/replacement can continuously or continually run.

[0493] Some embodiments of signal estimation can additionally include
discarding data that is considered sufficiently unreliable and/or
erroneous such that the data should not be used in a signal estimation
algorithm. In these embodiments, the system can be programmed to discard
outlier data points, for example data points that are so extreme that
they can skew the data even with the most comprehensive filtering or
signal estimation, and optionally replace those points with a projected
value based on historical data or present data (e.g., linear regression,
recursive filtering, and the like). One example of discarding sensor data
includes discarding sensor data that falls outside of a "Cone of
Possibility" such as described in more detail elsewhere herein. Another
example includes discarding sensor data when signal artifacts detection
detects values outside of a predetermined threshold (e.g., oxygen
concentration below a set threshold, temperature above a certain
threshold, signal amplitude above a certain threshold, etc). Any of the
signal estimation/replacement algorithms described herein can then be
used to project data values for those data that were discarded.

[0494] Analysis of signals from glucose sensors indicates at least two
types of noise, which are characterized herein as 1) system noise and 2)
signal artifacts, such as described in more detail above. It is noted
that system noise is easily smoothed using the algorithms provided
herein; however, the systems and methods described herein particularly
address signal artifacts, by replacing transient erroneous signal noise
caused by rate-limiting phenomenon (e.g., non-glucose related signal)
with estimated signal values, for example.

[0495] In certain embodiments of signal artifacts detection, oxygen
monitoring is used to detect whether transient non-glucose dependent
signal artifacts due to ischemia. Low oxygen concentrations in or near
the glucose sensor can account for a large part of the transient
non-glucose related signal artifacts as defined herein on a glucose
sensor signal, particularly in subcutaneously implantable glucose
sensors. Accordingly, detecting oxygen concentration, and determining if
ischemia exists can discover ischemia-related signal artifacts. A variety
of methods can be used to test for oxygen. For example, an oxygen-sensing
electrode, or other oxygen sensor can be employed. The measurement of
oxygen concentration can be sent to a processor, which determines if the
oxygen concentration indicates ischemia.

[0496] Additional description of signal artifact detection and replacement
can be found in U.S. Patent Publication Nos. 2005/0043598, 2007/0032706,
2007/0016381, and 2007/0066873, and U.S. patent application Ser. No.
11/762,638, filed on Jun. 13, 2007 and entitled "SYSTEMS AND METHODS FOR
REPLACING SIGNAL ARTIFACTS IN A GLUCOSE SENSOR DATA STREAM," all of which
are incorporated by reference herein in their entirety.

[0497] In one embodiment of signal artifacts detection that utilizes
examination or evaluation of the signal information content, filtered
(e.g., smoothed) data is compared to raw data (e.g., in sensor
electronics or in receiver electronics). In one such embodiment, a signal
residual is calculated as the difference between the filtered data and
the raw data. For example, at one time point (or one time period that is
represented by a single raw value and single filtered value), the
filtered data can be measured at 50,000 counts and the raw data can be
measured at 55,500 counts, which would result in a signal residual of
5,500 counts. In some embodiments, a threshold can be set (e.g., 5000
counts) that represents a first level of noise (e.g., signal artifact) in
the data signal, when the residual exceeds that level. Similarly, a
second threshold can be set (e.g., 8,000 counts) that represents a second
level of noise in the data signal. Additional thresholds and/or noise
classifications can be defined as is appreciated by one skilled in the
art. Consequently, signal filtering, processing, and/or displaying
decisions can be executed based on these conditions (e.g., the
predetermined levels of noise).

[0498] Although the above-described example illustrates one method of
determining a level of noise, or signal artifact(s), based on a
comparison of raw vs. filtered data for a time point (or single values
representative of a time period), a variety of alternative methods are
contemplated. In an alternative exemplary embodiment for determining
noise, signal artifacts are evaluated for noise episodes lasting a
certain period of time. For example, the processor (in the sensor or
receiver) can be configured to look for a certain number of signal
residuals above a predetermined threshold (representing noise time points
or noisy time periods) for a predetermined period of time (e.g., a few
minutes to a few hours or more).

[0499] In one exemplary embodiment, a processor is configured to determine
a signal residual by subtracting the filtered signal from the raw signal
for a predetermined time period. It is noted that the filtered signal can
be filtered by any known smoothing algorithm such as described herein,
for example a 3-point moving average-type filter. It is further noted
that the raw signal can include an average value, e.g., wherein the value
is integrated over a predetermined time period (such as 5-minutes).
Furthermore, it is noted that the predetermined time period can be a time
point or representative data for a time period (e.g., 5 minutes). In some
embodiments, wherein a noise episode for a predetermined time period is
being evaluated, a differential (delta residual) can be obtained by
comparing a signal residual with a previous signal residual (e.g., a
residual at time (t)=0 as compared to a residual at (t)-5 minutes.)
Similar to the thresholds described above with regard to the signal
residual, one or more thresholds can be set for the differentials,
whereby one or more differentials above one of the predetermined
differential thresholds defines a particular noise level. It has been
shown in certain circumstances that a differential measurement as
compared to a residual measurement as described herein, amplifies noise
and therefore may be more sensitive to noise episodes, without increasing
false positives due to fast, but physiological, rates of change.
Accordingly, a noise episode, or noise episode level, can be defined by
one or more points (e.g., residuals or differentials) above a
predetermined threshold, and in some embodiments, for a predetermined
period of time. Similarly, a noise level determination can be reduced or
altered when a different (e.g., reduced) number of points above the
predetermined threshold are calculated in a predetermined period of time.

[0500] In some embodiments, the amplitude of total signal, which can also
be described as power of the total signal, analyte signal (with or
without baseline (e.g., non-constant noise)), and/or non-constant noise,
is periodically or continuously obtained using methods such as are
described in more detail elsewhere herein (e.g., RMS method), wherein the
amplitude is a measure of the strength of the signal component. In some
embodiments, signal artifact events are detected by analysis of
amplitudes of various signal components, such as the amplitude of the
non-constant noise component as compared to the amplitude of the analyte
signal (with or without baseline).

[0501] In some embodiments, a start of a signal artifact event is
determined when the amplitude (power) of a signal artifact meets a first
predetermined condition. In one embodiment, the first predetermined
condition includes a residual amplitude of at least about 2, 3, 4, 5, 6,
7, 8, 9, 10, 12, 14, 16, 18, 20 or 25% of the total signal or analyte
signal amplitude (with or without baseline). In another embodiment, the
first predetermined condition includes a differential amplitude
(amplitude of a differential) of at least about 2, 3, 4, 5, 6, 7, 8, 9,
10, 12, 14, 16, 18, 20 or 25% of the total signal or analyte signal
amplitude (with or without baseline). In some embodiments, the first
predetermined condition includes a plurality of points (e.g.,
non-constant noise signal, residual, or differential) within a
predetermined period (e.g., 5, 10, 30, or 60 minutes) above a
predetermined threshold (e.g., an amplitude or a percentage amplitude),
wherein the plurality of points includes 2, 3, 4, 5, 6, 7, 8 or more
values.

[0502] In some embodiments, an end of a signal artifact event is
determined when then the amplitude (power) of a signal artifact meets a
second predetermined condition. In one embodiment, the second
predetermined condition includes a residual amplitude of no more than
about 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20 or 25% of the total
signal or analyte signal amplitude (with or without baseline). In another
embodiment, the second predetermined condition comprises a differential
amplitude of no more than about 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16,
18, 20 or 25% of the total signal or analyte signal amplitude (with or
without baseline). In some embodiments, the second predetermined
condition includes a plurality of points (e.g., non-constant noise
signal, residual, or differential) within a predetermined period (e.g.,
5, 10, 30, or 60 minutes) below a predetermined threshold (e.g., an
amplitude or a percentage amplitude), wherein the plurality of points
includes 2, 3, 4, 5, 6, 7, 8 or more values.

[0503] Preferably, the system is configured to use hysteresis to process
signals so that the output (start/end (on/off) of noise episodes) reacts
slowly by taking recent history into account; this prevents rapid
switching on and off as the glucose signal drifts around a threshold, for
example. In some embodiments, the first predetermined condition is
different from the second predetermined condition. In some embodiments,
the second condition includes criteria such as waiting a time period
(e.g., 20, 30, 40, 60, 90 minutes, or more) after the start of a noise
episode before determining an end of the noise episode. In some
embodiments, the second condition includes criteria such as waiting until
the signal increases beyond a predetermined threshold before determining
an end of the noise episode, wherein the predetermined threshold can be
higher than another threshold within the second condition that triggers
an end of the noise episode. In some embodiments, the first and second
conditions have different amplitude (power) thresholds. In some
embodiments, the first and second conditions have different window
lengths over which the evaluation is done. While not wishing to be bound
by theory, it is believed that condition(s) for determining a start of a
noise episode can be different from condition(s) for determining an end
of a noise episode. Accordingly, use of different start and end
thresholds can reduce toggling between start/end (or on/off) modes of the
noise episode too quickly. In one exemplary embodiment, the system is
configured to determine a start of a noise episode when the non-constant
noise is at least about 10% of the analyte signal and the system is
configured to determine an end of a noise episode at least about one hour
after the start of the noise episode and when the non-constant noise is
no more than about 10% of the analyte signal.

[0504] One or a plurality of the above signal artifacts detection models
can be used alone or in combination to detect signal artifacts such as
described herein. Accordingly, the data stream associated with the signal
artifacts can be discarded, replaced, or otherwise processed in order to
reduce or eliminate these signal artifacts and thereby improve the value
of the glucose measurements that can be provided to a user. Although much
of the following description is drawn to replacing signal artifacts,
circumstances exist wherein signal noise is too severe and/or too long in
duration to replace. In some embodiments, the system is configured to
determine whether a signal artifact and/or signal artifact episode has
exceeded a predetermined threshold. If the threshold is exceeded, then
data is not displayed (e.g., rather than replacing the signal as
described in more detail, below). In some embodiments, a signal artifact
and/or signal artifact episode threshold of (e.g., absolute threshold
and/or relative threshold such as high signal amplitude threshold, high
noise amplitude threshold, and/or percentage threshold) is used. In some
embodiments, a signal artifact and/or signal artifact episode threshold
of at least about 20, 30, 40, 60, 90, 120, 180 minutes or more duration
is used.

[0505] Reference is now made to FIG. 10, which is a flow chart 1000 that
illustrates the process of noise classification of the glucose sensor
signal in one embodiment. Preferably, the system is configured to
classify a level of noise in a signal obtained from a continuous analyte
sensor, for example, numerically and/or into groups. Advantageously,
classification of the noise level enables decision making of display,
calibration, alarming, sensor health/diagnostics, insulin delivery, and
the like.

[0506] At block 1010, a sensor data receiving module, also referred to as
the sensor data module, or processor module, receives sensor data (e.g.,
a data stream), including one or more time-spaced sensor data points
hereinafter referred to as "data stream," "sensor data," "sensor analyte
data", "signal," from a sensor via the receiver, which can be in wired or
wireless communication with the sensor. The sensor data receiving module
is described in more detail elsewhere herein, for example, with reference
to FIG. 5.

[0507] At block 1020, the processor module optionally filters the signal
received from the continuous analyte sensor. Filtering can be
accomplished by sensor electronics and/or the receiver electronics, which
are both encompassed by the term "computer system."

[0508] At block 1030, a noise classification module, also referred to as
the processor module, classifies a level of noise on the signal. In
general, one or more noise classification thresholds and/or criteria are
programmed within the computer system and/or adaptively determined by the
computer system.

[0509] In some embodiments, the signal noise is classified numerically
and/or by grouping. In one exemplary embodiment, the level of noise is
classified as light, medium, and heavy. In another exemplary embodiment,
the noise is classified as level1, level2, and level3, etc. However, any
types or numbers of classifications associated with predetermined
thresholds and/or criteria can be used. For example, in some embodiments,
the noise thresholds are predetermined (e.g., programmed into the
computer system (e.g., receiver)).

[0510] In some preferred embodiments, the noise thresholds are adaptively
determined by the computer system (e.g., for each sensor and/or
iteratively during a sensor session) based on a signal strength of the
sensor signal, which enables the noise thresholds to be customized to the
signal strength of the sensor and/or sensor in a particular host.

[0511] In one exemplary embodiment, the processor module applies a low
pass filter to the signal (raw or filtered) to determine a signal
strength thereof. Although a first order low pass filter may be preferred
in some embodiments, other orders (2nd, 3rd, etc) are also
possible.

[0512] In some further embodiments, for example, wherein the processor
module applies a low pass filter to the signal to determine a signal
strength, the system is configured to define one or more noise thresholds
for classification of the level of noise on the signal based at least in
part on a percentage of the signal strength (e.g., NoiseThreshold1
corresponds to a first percentage of signal strength, NoiseThreshold2
corresponds to a second percentage of signal strength, Noise Threshold3
corresponds to a third percentage of signal strength).

[0513] Conventionally, filters are configured to filter the signal to
provide an average of the signal. However, in some preferred embodiments,
the system is configured to "track the noise envelope," (i.e., to track
the outer amplitude of noise (e.g., without fluctuations) to obtain a
worst case scenario of noise on the sensor signal). In some embodiments,
low pass filters, RMS methods, and/or median filters applied to wide
windows of data (e.g., greater than or equal to about 30 min.) are
applied to the sensor signal. Advantageously, low pass filters track the
slow varying DC component (i.e., signal strength) of the analyte signal
and can be used to determine noise thresholds, which are described in
more detail, below.

[0514] In some embodiments, the processor module is configured to apply
one or more low pass filters to the noise signal to obtain one or more
noise indicators and subsequently compare the noise indicators with one
or more noise thresholds (either predefined noise thresholds or
adaptively determined noise thresholds). In the exemplary embodiment
wherein the system is configured to use a first order low pass filter to
define noise thresholds for noise classification, the system is further
configured to vary a coefficient of a low pass filter (e.g., the same or
another low pass filter), for example, by using the noise thresholds
defined as percentage of the signal strength, to detect noise of varying
thresholds. Namely, the processor module is configured to apply one or
more low pass filters to the noise signal to obtain one or more noise
indicators and compare the noise indicators with one or more noise
thresholds.

[0515] Additionally or alternatively, other methods of filtering, such as
determining a median point within a window of data, detecting a maximum
point within a window of data, and/or the like can be used to determined
the signal strength and/or classify noise based on thresholds.

[0516] In some alternative embodiments, a root mean square (RMS) method
can be used to determine signal strength.

[0517] In some alternative embodiments, spectral analysis can be used to
determine a signal strength and classify noise.

[0518] In some embodiments, the noise signal is a signal residual obtained
by comparing a raw signal to a filtered signal; however the differential
of the residual (delta residual), absolute delta residual, and/or the
like can also be used as the noise signal input to one or more of the
filters.

[0519] In some embodiments, the processor module is configured to use
hysteresis in classifying a level of noise in order to avoid waver
between noise classifications. For example, by defining different
criteria for returning back to a previous noise classification (e.g., a
greater number of data points (or time) in a noise level to return to a
previous level than to move to the level initially).

[0520] At block 1040, the output module, or processor module, is
configured to control an output based at least in part on the noise
classification determined at block 1030. In general, the system is
configured to control the output based on a level of resolution of the
signal, a level of confidence in the signal, and/or a level of
reliability of the signal.

[0521] In some embodiments, the system is configured to control a display
(e.g., a user interface 416) based at least in part on a noise
classification. In some embodiments, the system is configured to control
the display of raw and/or filtered data (e.g., on a user interface or
display) based at least in part on a noise classification. In some
embodiments, the system is configured to display rate of change
information based at least in part on a noise classification. In some
embodiments, the system is configured to control alarms indicative of at
least one of hypoglycemia, hyperglycemia, predicted hypoglycemia, and
predicted hyperglycemia based at least in part on a noise classification.
In some embodiments, the system is configured to controlling medicament
delivery (e.g., insulin delivery) and/or therapy instructions based at
least in part on a noise classification, for example, when to fall back
to a more conservative recommendation or when to open the loop (request
user interaction) of a closed loop delivery system. In some embodiments,
the system is configured to diagnose a sensor condition (e.g., sensor
failure) based at least in part on a noise classification. In some
embodiments, the system is configured to suspend display of sensor data
based at least in part on a noise classification. In some embodiments,
the system is configured to shut down a sensor session based at least in
part on a noise classification.

[0522] In some embodiments, the system is configured to display the noise
classification on the user interface 416. In some embodiments, the system
is configured to display information indicative of a level of noise on
the sensor signal (e.g., light/medium/heavy or level1/level2/level3). In
some embodiments, the system is configured to display information
indicative of an amount of time that the signal has been classified as
having a level of noise (e.g., a time-elapsed counter).

Signal Artifacts Replacement

[0523] One or a plurality of the above signal artifacts detection models
can be used alone or in combination to detect signal artifacts (e.g., a
level/classification of noise on the signal) such as described herein.
Accordingly, the data stream associated with the signal artifacts can be
discarded, replaced, or otherwise processed in order to reduce or
eliminate these signal artifacts and thereby improve the value of the
glucose measurements that can be provided to a user.

[0524] In some embodiments, Signal Artifacts Replacement can use systems
and methods that reduce or replace these signal artifacts that can be
characterized by transience, high frequency, high amplitude, and/or
substantially non-linear noise. Accordingly, a variety of filters,
algorithms, and other data processing are provided that address the
detected signal artifacts by replacing the data stream, or a portion of
the data stream, with estimated glucose signal values. It is noted that
"signal estimation" as used herein, is a broad term, which includes
filtering, data smoothing, augmenting, projecting, and/or other
algorithmic methods that estimate glucose signal values based on present
and historical data.

[0525] It is noted that a glucose sensor can contain a processor, and/or
the like, that processes periodically received raw sensor data (e.g.,
every 30 seconds). Although a data point can be available constantly, for
example by use of an electrical integration system in a chemo-electric
sensor, relatively frequent (e.g., every 30 seconds), or less frequent
data point (e.g., every 5 minutes), can be more than sufficient for
patient use. It is noted that according to the Nyquist Theory, a data
point is required about every 10 minutes to accurately describe
physiological change in glucose in humans. This represents the lowest
useful frequency of sampling. However, it should be recognized that it
can be desirable to sample more frequently than the Nyquist minimum, to
provide for sufficient data in the event that one or more data points are
lost, for example. Additionally, more frequently sampled data (e.g.,
30-second) can be used to smooth the less frequent data (e.g., 5-minute)
that are transmitted. It is noted that in this example, during the course
of a 5-minute period, 10 determinations are made at 30-second intervals.

[0526] In some embodiments of Signal Artifacts Replacement, signal
estimation can be implemented in the sensor and transmitted to a receiver
for additional processing. In some embodiments of Signal Artifacts
Replacement, raw data can be sent from the sensor to a receiver for
signal estimation and additional processing therein. In some embodiments
of Signal Artifacts Replacement, signal estimation is performed initially
in the sensor, with additional signal estimation in the receiver.

[0527] In some embodiments of Signal Artifacts Replacement, wherein the
sensor is an implantable glucose sensor, signal estimation can be
performed in the sensor to ensure a continuous stream of data. In
alternative embodiments, data can be transmitted from the sensor to the
receiver, and the estimation performed at the receiver; It is noted
however that there can be a risk of transmit-loss in the radio
transmission from the sensor to the receiver when the transmission is
wireless. For example, in embodiments wherein a sensor is implemented in
vivo, the raw sensor signal can be more consistent within the sensor (in
vivo) than the raw signal transmitted to a source (e.g., receiver)
outside the body (e.g., if a patient were to take the receiver off to
shower, communication between the sensor and receiver can be lost and
data smoothing in the receiver would halt accordingly). Consequently, It
is noted that a multiple point data loss in the filter can take for
example, about 25 to about 40 minutes for the data to recover to near
where it would have been had there been no data loss.

[0528] In some embodiments of Signal Artifacts Replacement, signal
estimation is initiated only after signal artifacts are positively
detected and stopped once signal artifacts are negligibly detected. In
some alternative embodiments signal estimation is initiated after signal
artifacts are positively detected and then stopped after a predetermined
time period. In some alternative embodiments, signal estimation can be
continuously or continually performed. In some alternative embodiments,
one or more forms of signal estimation can be accomplished based on the
severity of the signal artifacts, such as described in more detail with
reference to U.S. Patent Publication Nos. 2005/0043598, 2007/0032706,
2007/0016381, and 2007/0066873, and co-pending U.S. patent application
Ser. No. 11/762,638, filed on Jun. 13, 2007 and entitled "SYSTEMS AND
METHODS FOR REPLACING SIGNAL ARTIFACTS IN A GLUCOSE SENSOR DATA STREAM,"
all of which are incorporated herein by reference in their entirety.

[0529] In some embodiments of Signal Artifacts Replacement, the processor
module performs a linear regression. In one such implementation, the
processor module performs a linear regression analysis of the n (e.g.,
10) most recent sampled sensor values to smooth out the noise. A linear
regression averages over a number of points in the time course and thus
reduces the influence of wide excursions of any point from the regression
line. Linear regression defines a slope and intercept, which is used to
generate a "Projected Glucose Value," which can be used to replace sensor
data. This regression can be continually performed on the data stream or
continually performed only during the transient signal artifacts. In some
alternative embodiments, signal estimation can include non-linear
regression.

[0530] In another embodiment of Signal Artifacts Replacement, the
processor module performs a trimmed regression, which is a linear
regression of a trimmed mean (e.g., after rejecting wide excursions of
any point from the regression line). In this embodiment, after the sensor
records glucose measurements at a predetermined sampling rate (e.g.,
every 30 seconds), the sensor calculates a trimmed mean (e.g., removes
highest and lowest measurements from a data set and then regresses the
remaining measurements to estimate the glucose value.

[0531] In another embodiment of Signal Artifacts Replacement, the
processor module runs a non-recursive filter, such as a finite impulse
response (FIR) filter. A FIR filter is a digital signal filter, in which
every sample of output is the weighted sum of past and current samples of
input, using only some finite number of past samples.

[0532] In another embodiment of Signal Artifacts Replacement, the
processor module runs a recursive filter, such as an infinite impulse
response (IIR) filter. An IIR filter is a type of digital signal filter,
in which every sample of output is the weighted sum of past and current
samples of input. In one exemplary implementation of an IIR filter, the
output is computed using 6 additions/subtractions and 7 multiplications
as shown in the following equation:

This polynomial equation includes coefficients that are dependent on
sample rate and frequency behavior of the filter. Frequency behavior
passes low frequencies up to cycle lengths of 40 minutes, and is based on
a 30 second sample rate. In alternative implementations, the sample rate
and cycle lengths can be more or less. See Lynn "Recursive Digital
Filters for Biological Signals" Med. & Biol. Engineering, Vol. 9, pp.
37-43, which is incorporated herein by reference in its entirety.

[0533] In another embodiment of Signal Artifacts Replacement, the
processor module runs a maximum-average (max-average) filtering
algorithm. The max-average algorithm smoothes data based on the discovery
that the substantial majority of signal artifacts observed after
implantation of glucose sensors in humans, for example, is not
distributed evenly above and below the actual blood glucose levels. It
has been observed that many data sets are actually characterized by
extended periods in which the noise appears to trend downwardly from
maximum values with occasional high spikes such as described in more
detail above with reference to FIG. 7C, section 74b, which is likely in
response to limitations in the system that do not allow the glucose to
fully react at the enzyme layer and/or proper reduction of H2O2
at the counter electrode, for example. To overcome these downward
trending signal artifacts, the max-average calculation tracks with the
highest sensor values, and discards the bulk of the lower values.
Additionally, the max-average method is designed to reduce the
contamination of the data with non-physiologically high data from the
high spikes.

[0534] In another embodiment of Signal Artifacts Replacement, the
processor module runs a "Cone of Possibility Replacement Method." It is
noted that this method can be performed in the sensor and/or in the
receiver. The Cone of Possibility Detection Method utilizes physiological
information along with glucose signal values in order define a "cone" of
physiologically feasible glucose signal values within a human.
Particularly, physiological information depends upon the physiological
parameters obtained from continuous studies in the literature as well as
our own observations. A first physiological parameter uses a maximal
sustained rate of change of glucose in humans (e.g., about 4 to 5
mg/dl/min) and a maximum sustained acceleration of that rate of change
(e.g., about 0.1 to 0.2 mg/min/min). A second physiological parameter
uses the knowledge that rate of change of glucose is lowest at the maxima
and minima, which are the area of greatest risk in patient treatment,
such as described with reference to Cone of Possibility Detection, above.
A third physiological parameter uses the fact that the best solution for
the shape of the curve at any point along the curve over a certain time
period (e.g., about 20-25 minutes) is a straight line. It is noted that
the maximum rate of change can be narrowed in some instances. Therefore,
additional physiological data can be used to modify the limits imposed
upon the Cone of Possibility Replacement Method for sensor glucose
values. For example, the maximum per minute rate change can be lower when
the subject is lying down or sleeping; on the other hand, the maximum per
minute rate change can be higher when the subject is exercising, for
example.

[0535] The Cone of Possibility Replacement Method utilizes physiological
information along with blood glucose data in order to improve the
estimation of blood glucose values within a human in an embodiment of
Signal Artifacts Replacement. The Cone of Possibility Replacement Method
can be performed on raw data in the sensor, on raw data in the receiver,
or on smoothed data (e.g., data that has been replaced/estimated in the
sensor or receiver by one of the methods described above) in the
receiver.

[0536] In other embodiments of Signal Artifacts Replacement, prediction
algorithms, also referred to as projection algorithms, can be used to
replace glucose data signals for data which does not exist because 1) it
has been discarded, 2) it is missing due to signal transmission errors
and the like, or 3) it represents a time period (e.g., future) for which
a data stream has not yet been obtained based on historic and/or present
data. Prediction/projection algorithms include any of the above described
Signal Artifacts Replacement algorithms, and differ only in the fact that
they are implemented to replace time points/periods during which no data
is available (e.g., for the above-described reasons), rather than
including that existing data, within the algorithmic computation.

[0537] In some embodiments, signal replacement/estimation algorithms are
used to predict where the glucose signal should be, and if the actual
data stream varies beyond a certain threshold of that projected value,
then signal artifacts are detected. In alternative embodiments, other
data processing can be applied alone, or in combination with the
above-described methods, to replace data signals during system noise
and/or signal artifacts.

[0538]FIG. 11 is a flow chart 1100 that illustrates the process of
detecting and processing signal artifacts in some embodiments.

[0539] At block 1102, a sensor data receiving module, also referred to as
the sensor data module, or processor module, receives sensor data (e.g.,
a data stream), including one or more time-spaced sensor data points. In
some embodiments, the data stream is stored in the sensor for additional
processing; in some alternative embodiments, the sensor periodically
transmits the data stream to the receiver, which can be in wired or
wireless communication with the sensor. In some embodiments, raw and/or
filtered data is stored in the sensor and/or transmitted and stored in
the receiver, as described in more detail elsewhere herein.

[0540] At block 1104, a signal artifacts detection module, also referred
to as the signal artifacts detector, or signal reliability module, is
programmed to detect transient non-glucose related signal artifacts in
the data stream. In some embodiments, the signal artifacts detector can
comprise an oxygen detector, a pH detector, a temperature detector,
and/or a pressure/stress detector, for example, the signal artifacts
detector 228 in FIG. 2. In some embodiments, the signal artifacts
detector is located within the processor 214 (FIG. 2) and utilizes
existing components of the glucose sensor to detect signal artifacts, for
example by pulsed amperometric detection, counter electrode monitoring,
reference electrode monitoring, and frequency content monitoring, which
are described elsewhere herein. In yet other embodiments, the data can be
sent from the sensor to the receiver which comprises programming in the
processor 406 (FIG. 4) that performs algorithms to detect signal
artifacts, for example such as described with reference to "Cone of
Possibility Detection" method and/or by comparing raw data vs. filtered
data, both of which are described in more detail elsewhere herein.

[0541] In some exemplary embodiments, the processor module in either the
sensor electronics and/or the receiver electronics evaluates an
intermittent or continuous signal-to-noise measurement to determine
aberrancy of sensor data responsive to a signal-to-noise ratio above a
set threshold. In some exemplary embodiments, signal residuals (e.g., by
comparing raw and filtered data) are intermittently or continuously
analyzed for noise above a set threshold. In some exemplary embodiments,
pattern recognition can be used to identify noise associated with
physiological conditions, such as low oxygen, or other known signal
aberrancies. Accordingly, in these embodiments, the system can be
configured, in response to aberrancies in the data stream, to trigger
signal estimation, adaptively filter the data stream according to the
aberrancy, and the like, as described in more detail elsewhere herein.

[0542] In some embodiments, one or more signal residuals are obtained by
comparing received data with filtered data, whereby a signal artifact can
be determined. In some embodiments, a signal artifact event is determined
to have occurred if the residual is greater than a threshold. In some
exemplary embodiments, another signal artifact event is determined to
have occurred if the residual is greater than a second threshold. In some
exemplary embodiments, a signal artifact event is determined to have
occurred if the residual is greater than a threshold for a period of time
or an amount of data. In some exemplary embodiments, a signal artifact
event is determined to have occurred if a predetermined number of signal
residuals above a predetermined threshold occur within a predetermined
time period (or an amount of data). In some exemplary embodiments, an
average of a plurality of residuals is evaluated over a period of time or
amount of data to determine whether a signal artifact has occurred. The
use of residuals for noise detection can be preferred in circumstances
where data gaps (non-continuous) data exists.

[0543] In some exemplary embodiments, a differential, also referred to as
a derivative of the residual (delta residual), is determined by comparing
a first residual (e.g., at a first time point) and a second residual
(e.g., at a second time point), wherein a signal artifact event is
determined to have occurred when the differential is above a
predetermined threshold. In some exemplary embodiments, a signal artifact
event is determined to have occurred if the differential is greater than
a threshold for a period of time or amount of data. In some exemplary
embodiments, an average of a plurality of differentials is calculated
over a period of time or amount of data to determine whether a signal
artifact has occurred.

[0544] Numerous embodiments for detecting signal artifacts are described
in more detail in the section entitled, "Signal Artifacts Detection," all
of which are encompassed by the signal artifacts detection at block 1104.

[0545] At block 1106, the processor module is configured to process the
sensor data based at least in part on whether the signal artifact event
has occurred.

[0546] In some embodiments, the sensor data is filtered in the receiver
processor to generate filtered data if the signal artifact event is
determined to have occurred; filtering can be performed either on the raw
data, or can be performed to further filter received filtered data, or
both.

[0547] In some embodiments, signal artifacts detection and processing is
utilized in outlier detection, such as described in more detail elsewhere
herein, wherein a disagreement between time corresponding reference data
and sensor data can be analyzed, e.g., noise analysis data (e.g., signal
artifacts detection and signal processing) can be used to determine which
value is likely more reliable (e.g., whether the sensor data and/or
reference data can be used for processing). In some exemplary embodiments
wherein the processor module receives reference data from a reference
analyte monitor, a reliability of the received data is determined based
on signal artifacts detection (e.g., if a signal artifact event is
determined to have occurred.) In some exemplary embodiments, a
reliability of the sensor data is determined based on signal artifacts
detection (e.g., if the signal artifact event is determined to have not
occurred.) The term "reliability," as used herein, is a broad term and is
used in its ordinary sense, including, without limitation, a level of
confidence in the data (e.g., sensor or reference data), for example, a
positive or negative reliance on the data (e.g., for calibration,
display, and the like) and/or a rating (e.g., of at least 60%, 70%, 80%,
90% or 100% confidence thereon.)

[0548] In some embodiments wherein a matched data pair is formed by
matching reference data to substantially time corresponding sensor data
(e.g., for calibration and/or outlier detection) described in more detail
elsewhere herein, matching of a data pair can be configured to occur
based on signal artifacts detection (e.g., only if a signal artifact
event is determined to have not occurred.) In some embodiments wherein
the reference data is included in a calibration factor for use in
calibration of the glucose sensor as described in more detail elsewhere
herein, the reference data can be configured to be included based on
signal artifacts detection (e.g., only if the signal artifact event is
determined to have not occurred.) In general, results of noise analysis
(e.g., signal artifact detection and/or signal processing) can be used to
determine when to use or eliminate a matched pair for use in calibration
(e.g., calibration set).

[0549] In some embodiments, a user is prompted for a reference glucose
value based on signal artifacts detection (e.g., only if a signal
artifact event is determined to have not occurred.) While not wishing to
be bound by theory, it is believed certain more preferable times for
calibration (e.g., not during noise episodes) can be detected and
processed by prompting the user for calibration during those times.

[0550] In some embodiments, results of noise analysis (e.g., signal
artifact detection and/or signal processing) can be used to determine how
to process the sensor data. For example, different levels of signal
processing and display (e.g., raw data, integrated data, filtered data
utilizing a first filter, filtered data utilizing a second filter, which
may be "more aggressive" than the first filter by filtering over a larger
time period, and the like.) Accordingly, the different levels of signal
processing and display can be selectively chosen responsive to a
reliability measurement, a positive or negative determination of signal
artifact, and/or signal artifacts above first and second predetermined
thresholds.

[0551] In some embodiments, results of noise analysis (e.g., signal
artifact detection and/or signal processing) can be used to determine
when to utilize and/or display different representations of the sensor
data (e.g., raw vs. filtered data), when to turn filters on and/or off
(e.g., processing and/or display of certain smoothing algorithms), and/or
when to further process the sensor data (e.g., filtering and/or
displaying). In some embodiments, the display of the sensor data is
dependent upon the determination of signal artifact(s). For example, when
a certain predetermined threshold of signal artifacts have been detected
(e.g., noisy sensor data), the system is configured to modify or turn off
a particular display of the sensor data (e.g., display filtered data,
display processed data, disable display of sensor data, display range of
possible data values, display indication of direction of glucose trend
data, replace sensor data with predicted/estimated sensor data, and/or
display confidence interval representative of a level of confidence in
the sensor data.) In some exemplary embodiments, a graphical
representation of filtered sensor data is displayed if the signal
artifact event is determined to have occurred. Alternatively, when a
certain predetermined threshold of signal artifacts has not been detected
(e.g., minimal, insignificant, or no noise in the data signal), the
system is configured to modify or turn on a particular display of the
sensor data (e.g., display unfiltered (e.g., raw or integrated) data, a
single data value, an indication of direction of glucose trend data,
predicted glucose data for a future time period and/or a confidence
interval representative of a level of confidence in the sensor data.)

[0552] In some embodiments wherein a residual (or differential) is
determined as described in more detail elsewhere herein, the residual (or
differential) is used to modify the filtered data during signal artifact
event(s). In one such exemplary embodiment, the residual is measured and
then added back into the filtered signal. While not wishing to be bound
by theory, it is believed that some smoothing algorithms may result in
some loss of dynamic behavior representative of the glucose
concentration, which disadvantage may be reduced or eliminated by the
adding of the residual back into the filtered signal in some
circumstances.

[0553] In some embodiments, the sensor data can be modified to compensate
for a time lag, for example by predicting or estimating an actual glucose
concentration for a time period considering a time lag associated with
diffusion of the glucose through the membrane, digital signal processing,
and/or algorithmically induced time lag, for example.

[0554]FIG. 12 is a graph that illustrates a raw data stream from a
glucose sensor for approximately 24 hours with a filtered version of the
same data stream superimposed on the same graph. Additionally, this graph
illustrates a noise episode, the beginning and end of which was detected
by a noise detection algorithm of the preferred embodiments, and during
which a particular filter was applied to the data. The x-axis represents
time in minutes; the y-axis represents the raw and filtered data values
in counts. In this example, the raw data stream was obtained in 5 minute
intervals from a transcutaneous glucose sensor such as described in more
detail above, with reference to FIG. 1B and in U.S. Patent Publication
No. US-2006-00201087-A1.

[0555] In section 1202 of the data, which encompasses an approximately 14
hour period up to time=2:22, the filtered data was obtained by applying a
3-point moving average window to the raw data. During that period, the
noise detection algorithm was applied to detect a noise episode. In this
example, the algorithm included the following: calculating a residual
signal by subtracting the filtered data from the raw data (e.g., for each
5-minute point); calculating a differential by subtracting the residual
for each 5-minute point from its previous 5-minute residual; determining
if each differential exceeds a threshold of 5000 counts (and declaring a
noisy point if so); and determining whether 6 out of 12 points in the
past 1 hour exceed that threshold (and declaring a noise episode if so).
Accordingly, a noise episode was declared at time=2:22 and a more
aggressive filter was applied as described with reference to section
1204.

[0556] In section 1204 of the data, also referred to as a noise episode,
which encompasses an approximately 51/2 hour period up to time=7:57, the
filtered data was obtained by applying a 7-point moving average window to
the raw data. The 7-point moving average window was in this example was
an effective filter in smoothing out the noise in the data signal as can
be seen on the graph. During that period, an algorithm was applied to
detect when the noise episode had ended. In this example, the algorithm
included the following: calculating a residual signal by subtracting the
filtered data (using the 3-point moving average filter described above)
from the raw data (e.g., for each 5-minute point); calculating a
differential of the residual by subtracting the residual for each
5-minute point from its previous 5-minute residual; determining if each
differential exceeds a threshold of 5000 counts (and declaring a noisy
point if so); and determining whether less than 2 noisy points had
occurred in the past hour (and declaring the noise episode over if so).
Accordingly, the noise episode was declared as over at time-7:57 and the
less aggressive filter (e.g., 3-point moving average) was again applied
with the noise detection algorithm as described with reference to section
1202, above.

[0557] In section 1206 of the data, which encompasses more than 4 hours of
data, the filtered data was obtained by applying a 3-point moving average
window to the raw data. During that period, the noise detection algorithm
(described above) did not detect a noise episode. Accordingly, raw or
minimally filtered data could be displayed to the patient during this
time period.

[0558] It was shown that the above-described example provided smoother
glucose information during noise episodes, by applying a more aggressive
filter to smooth out the noise. It is believed that when displayed, the
smoother data will avoid presenting potentially misleading or inaccurate
information to the user. Additionally, it was shown in the
above-described example that during non-noisy periods (when noise
episodes are not detected), raw or less aggressively filtered data can be
displayed to the user in order to provide more accurate data with minimal
or no associated filter-induced time lag in the data. Furthermore, it is
believed that proper detection of noise episodes aids in determining
proper times for calibration, ensuring more accurate calibration than may
otherwise be possible.

[0559] In the above-described example, the criteria for the onset & offset
of noise episodes were different; for example, the onset criteria
included 6 out of 12 points in the past 1 hour exceeding a threshold,
while the offset criteria included less than 2 noisy points in the past 1
hour. In this example, these different criteria were found to create
smoother transitions in the data between the raw and filtered data and
avoided false detections of noise episodes.

[0560]FIG. 13 is a flowchart 1300 that illustrates a process for
determining a rate of change of a continuous analyte sensor signal, in
one embodiment.

[0561] At block 1302, a sensor data receiving module, also referred to as
the sensor data module, computer system, or processor module, receives
sensor data (e.g., a data stream), including one or more time-spaced
sensor data points hereinafter referred to as "data stream," "sensor
data," "sensor analyte data", "signal," from a sensor via the receiver,
which can be in wired or wireless communication with the sensor. The
sensor data receiving module is described in more detail elsewhere
herein, for example, with reference to FIG. 5.

[0562] At block 1304, optionally determining a level of noise on the
sensor signal, which is described in more detail elsewhere herein.

[0563] At block 1306, the computer system (e.g., processor module)
calculates a rate of change for a window of sensor data, wherein the
window of sensor data includes two or more sensor data points. In some
embodiments, the window of sensor data is a user selectable time period.
In some embodiments, the window of sensor data is a programmable time
period. In some embodiments, wherein the window of sensor data adaptively
adjusts based at least in part on a level of noise in the sensor data.
Accordingly, one or more windows of data can be user-selected (or
adaptively-selected by the computer system) depending upon what type of
trend data is to be displayed. As one example of a window of data, a
"current trend" includes rate of change information from recent data
(e.g., within about 5, 10, 15, 20, 25, 30, 35, 40 minutes). As another
example of a window of data, a "sustained trend" includes rate of change
information from a wider window of data than the current trend (e.g.,
within about 20, 30, 40, 50, 60 or more minutes).

[0564] In some embodiments, the computer system is configured to use
either raw sensor data or filtered sensor data (including adaptive
filtering) in the rate of change calculation depending at least in part
upon the level of noise determined. In some embodiments, the rate of
change calculation comprises calculating at least two rate of change
calculations, and filtering the rate of change calculation to obtain a
filtered rate of change value as described in more detail elsewhere
herein. In some embodiments, the rate of change calculation comprises
calculating at least two point-to-point rate of change calculations, and
wherein the rate of change calculation further comprises adaptively
selecting a filter to apply to the point-to-point rate of change
calculation based at least in part on the level of noise determined.

[0565] In some embodiments, the rate of change calculation described
herein is used to predict one or more analyte values, which is described
in more detail with reference to FIG. 14, for example. In some
embodiments, a trend arrow is displayed on the user interface based at
least in part on the rate of change calculation described herein. In some
embodiments, the rate of change calculation described herein is issued to
determine a therapy instruction, for example, a medicament delivery type
and/or amount of medicament for delivery via an open-loop, semi-open loop
and/or closed loop system.

[0566]FIG. 14 is a flowchart 1400 that illustrates a process for
prediction based on a continuous analyte sensor signal, in one
embodiment, including determining whether to trigger a predicted
hypoglycemia or predicted hyperglycemia alarm and/or to display a
predicted time to predicted hypoglycemia or predicted hyperglycemia. In
the embodiment described herein with reference to FIG. 13, a "free flow
algorithm" is used for the predictive algorithm, which is in contrast to
a conventional predictive algorithms that use model or curve-fitting type
algorithms. Advantageously, the free flow algorithm described herein is
robust to the non-stationary condition of signal.

[0567] At block 1402, a sensor data receiving module, also referred to as
the sensor data module, computer system, or processor module, receives
sensor data (e.g., a data stream), including one or more time-spaced
sensor data points hereinafter referred to as "data stream," "sensor
data," "sensor analyte data", "signal," from a sensor via the receiver,
which can be in wired or wireless communication with the sensor. The
sensor data receiving module is described in more detail elsewhere
herein, for example, with reference to FIG. 5.

[0568] At block 1404, the computer system optionally filters the sensor
data to obtain an estimated sensor value (e.g., calibrated glucose
concentration based on sensor data). In some embodiments, the estimated
sensor value is at a time t=0; for example, the computer system
compensates for time lag associated with filtering and/or the sensor.
However, the computer system can additionally and/or alternatively
compensate for a time lag in other processing step or modules, such as
block 1410.

[0569] At block 1406, the computer system calculates a rate of change
based on a time series analysis of rate of change information, wherein
the time series analysis includes at least two rate of change values. In
some embodiments, the rate of change values can be obtained by gradient
tracking, multiple rate of change calculations, point-to-point rate of
change calculations, and/or the like. In one exemplary embodiment, the
computer system calculates at least two point-to-point rate of change
values, as described in more detail elsewhere herein.

[0570] At block 1408, the computer system filters the at least two rate of
change values to obtain a filtered ROC value. In some embodiments, the
computer system continuously filters the at least two point-to-point rate
of change values to obtain a filtered ROC value.

[0571] At block 1410, the computer system determines whether to trigger a
predicted hypoglycemia or predicted hyperglycemia alarm and/or display a
predicted time to predicted hypoglycemia or predicted hyperglycemia. For
example, in some embodiments, the computer system determines a predicted
value for a future time period based on the estimated sensor value, the
filtered ROC value and a time to the future time period, which can be
programmed into the computer or user selectable (e.g., 5, 10, 15, 20
minutes or more). In some embodiments, the computer system compares the
predicted value against a threshold (e.g., 50, 60, 70, 80, 90 or 100
mg/dL for predicted hypoglycemia and/or 160, 180, 200, 220 or 240 for
predicted hyperglycemia), which can be programmed into the computer
system or user selectable. In some embodiments, the computer system
triggers an alarm when the predicted value passes the threshold.

[0572] In some embodiments, the computer system determines a predicted
time to a threshold, wherein the predicted time is based at least in part
on the estimated sensor value, the filtered ROC value and a threshold
(e.g., 50, 60, 70, 80, 90 or 100 mg/dL for predicted hypoglycemia and/or
160, 180, 200, 220 or 240 for predicted hyperglycemia), which can be
programmed into the computer system or user selectable. In some
embodiments, the computer system is configured to display the predicted
time to threshold on a user interface. In some embodiments, the computer
system is configured to display the predicted time to threshold only when
the predicted time is below a predetermined value.

[0573] In some embodiments, the computer system determines an insulin
therapy based at least in part on the filtered ROC value. In some
embodiments, the computer system displays a trend arrow on a user
interface based at least in part on the filtered ROC value.

[0574] In some embodiments, a trend arrow is displayed on the user
interface based at least in part on the filtered rate of change
calculation described herein. In some embodiments, the filtered rate of
change calculation described herein is issued to determine a therapy
instruction, for example, a medicament delivery type and/or amount for
delivery via an open-loop, semi-open loop and/or closed loop system.

[0575] Reference is now made to FIG. 15, which is a flow chart 1500 that
illustrates the process of receiving sensor data, setting a mode and
further processing data based upon the mode. In general, the modes of the
preferred embodiments enable systems and methods associated with
processing analyte data, alarms, medicament delivery, at the like, to be
adapted to and/or customized for a user's mode (e.g., activity,
physiological condition and/or preference). In one embodiment, having
different modes allows the system to evaluate and/or process the analyte
data, (e.g., concentration, trends, etc) using additional information
regarding the user's activity and/or preference (or "mode"). For example,
when a person is exercising, his/her glucose levels may increase or
decrease in trends that would be abnormal under any other circumstances;
by setting the appropriate mode, the system is configured to modify its
processing associated with the user in a particular mode, e.g., "exercise
mode" to provide alarms, analyte estimates, trend information, therapy
recommendations, and the like, customized with one or more criteria
associated with exercise.

[0576] In some preferred embodiments, systems and methods are provided to
account for the various events that can occur in the life of the user
and/or the preferences (e.g. the user simply not wanting to be disturbed)
of the user to determine whether alarms and/or medicament delivery
instructions are necessary in response to the user's glucose data.
Depending upon the event that the user has scheduled or the user's
preference, the user may or may not want to be alarmed of certain analyte
values and/or trends in their glucose levels and therefore, by setting a
mode, the user can control a sensitivity of the alarms (e.g., high,
medium, low) to dictate how often the user is alarmed. For example, when
a user is sleeping he/she may not want to be alarmed of levels and/or
changes in glucose unless they are of urgent need (e.g. low sensitivity),
accordingly, the systems is configured to alter alarm criteria to be less
sensitive during "resting mode."

[0577] At block 1510, a sensor data receiving module, also referred to as
the sensor data module, or processor module, receives sensor data (e.g.,
a data stream), including one or more time-spaced sensor data points
hereinafter referred to as "data stream," "sensor data," "sensor analyte
data", "signal," from a sensor via the receiver, which can be in wired or
wireless communication with the sensor. The sensor data receiving module
is described in more detail elsewhere herein, for example, with reference
to FIG. 5.

[0578] At block 1520, a mode setting module, sets the mode of the system.
In preferred embodiments, the mode is set based at least in part upon one
or more inputs (e.g. buttons, menus) and/or data received from various
devices (e.g. accelerometer, temperature sensor, timer, mode profile,
scheduling software). In the preferred embodiment, the system, at least
in part, uses the data received from inputs and/or devices to set a mode
from a plurality of predetermined modes (e.g. resting mode, do not
disturb mode, exercise mode, illness mode, menstruation mode, mealtime
mode, snooze mode, day mode, night mode, hyperglycemia mode, hypoglycemia
mode, clinical risk mode, noise mode, and the like). In general, each
mode correlates to an activity, event, physiological condition, sensor
condition, and/or preference of the user.

[0579] In some embodiments, the system is configured to set the mode at
least in part responsive to receipt of a user input. In an exemplary
embodiment, the system comprises one or more buttons, and wherein the
processor module is configured to receive the user input by selection of
one or more buttons (e.g., dedicated mode buttons or universal buttons
that enable selection from a user interface). In another exemplary
embodiment, the system comprises a screen configured to display one or
more menus and receive the user input by selection of one or more items
from the one or more menus. In some embodiments, the system is configured
to operably connect (e.g. via wired connection, wireless connection) with
another computer system (e.g. mobile phone, personal digital assistant,
personal computer, and the like) such that data (e.g. modes, mode
profiles) can be transmitted to the system of the preferred embodiments.
In an exemplary embodiment, the system is operably connected using a
wired connection (e.g. cat 5, USB). In yet another exemplary embodiment,
the system is operably connected using a wireless connection.
Advantageously, setting of modes as described in the preferred
embodiments enables the user to switch preferences and/or criteria
associated with alarms, therapy instruction, data processing, and/or the
like, to correspond with the user's life quickly and easily.

[0580] In some embodiments, the system is configured to set a mode
responsive to programming configured to schedule and organize events on a
calendar (e.g. Microsoft Outlook, Eudora). In another embodiment, the
system is further configured to set the mode at least in part responsive
to a mode profile, wherein the system or the user can set the mode
profile. For example, a "work week" mode profile would have defined modes
that correspond to the user's usual schedule during a 5-day work week.
Mode profiles can be default system profiles, customizable default system
profiles, user definable profiles, and the like. Accordingly, the
embodiments described herein allow the user to schedule a series of
time-based modes that occur on a recurring basis.

[0581] In some embodiments, the system is configured to automatically set
the mode at least in part responsive to a comparison of data with one or
more criteria (e.g. accelerometer, temperature sensor and/or criteria
associated with the adaptive mode learning module as described in more
detail herein). In an exemplary embodiment, the system includes and/or is
configured to receive data from an accelerometer. The term
"accelerometer" as used herein is a broad term and is to be given its
ordinary and customary meaning to a person of ordinary skill in the art
(and is not limited to a special or customized meaning), and furthermore
refers without limitation to a device that monitors movement. In another
exemplary embodiment, the system includes and/or is configured to receive
data from a temperature sensor.

[0582] In one exemplary embodiment, the system comprises programming
configured to automatically set the mode at least in part responsive to
an adaptive mode-learning module (e.g. within the processor module).
Preferably, the adaptive mode-learning module is configured to process
(historic) sensor data and time-corresponding modes over time and
determine patterns or trends used to automatically set a mode when
similar trends or patterns are detected on real-time sensor data. For
example, the system is configured to adaptively switch in and out of
modes without constant user interaction by comparing real time data with
historic data.

[0583] In yet another exemplary embodiment, the system comprises a timer
associated with one or more modes, wherein the timer is configured to set
the mode for a predetermined amount of time (e.g. 20 minutes, 30 minutes,
1 hour, 4 hours, 8 hours, etc.). Either the user or the system can set
the timer. In some embodiments, the timer is a default timer that allows
a user to select a mode with a single click for a predetermined time
period. In general, the system is configured to have any combination of
automatic timers, default timers, user settable times, profile driven
timers, and/or the like, for any combination of one or more modes.

[0584] In one embodiment, the system is configured to classify a level of
noise in the continuous analyte sensor data, as described in more detail
elsewhere herein. The level of noise can be an indicator of a level of
accuracy of the sensor data and can be user to set a mode (e.g.
automatically by the system) responsive at least in part to the level of
noise.

[0585] At block 1530, continuous sensor data is received and processed
based at least in part on the mode. In general, each mode is associated
with one or more criteria and/or inputs (e.g. alarm criteria and/or types
of alarms, parameters associated with calculating a therapy instruction,
and/or processing instructions associated with estimating analyte values
and/or outputting analyte sensor information). In general, modes provide
customized processing of sensor data associated with an activity, event,
physiological condition, sensor condition, and/or preference of a user,
for example.

[0586] In some embodiments, the system is configured to determine a type
or alarm to activate (e.g. audible sound, visual display, vibration,
alphanumeric message, and/or wireless transmission) based at least in
part on the mode. In some embodiments, the system is configured to alter
the alarm criteria (e.g., threshold analyte values, rate of change,
and/or acceleration).

[0587] In some embodiments, the system comprises a therapy module (e.g.
processor module) configured to determine a therapy instruction (e.g. a
quantified dosage of a medicament, an activity, a recommend caloric
intake) based at least in part on the mode. In some embodiments, the
system is operably connected with a medicament delivery device (e.g. a
device used to deliver a dose of insulin to the user), wherein the
medicament type and/or amount to be delivered is based at least in part
on the mode. In some embodiments, the system is configured to require a
validation of the medicament delivery instruction prior to delivery of
the medicament (e.g. insulin) based at least in part on the mode.
Advantageously, the therapy instructions of the preferred embodiment are
customizable for an activity, event, physiological condition, sensor
condition, and/or preference of a user.

[0588] In some embodiments, the estimation of analyte values is based at
least in part on a mode. In one such exemplary embodiment, the system is
configured to aggressively filter data during "night mode" because the
trade off of a time delay associated with aggressively filtered data
versus reduced false alarms caused by noise spikes in unfiltered data
would be advantageous to a sleeping patient. In some embodiments, the
user interface is controlled, at least in part based on the mode.

[0589] As one example, resting mode sets a reduced sensitivity of alarms
(e.g., hypoglycemic alarms with analyte thresholds that are 5%, 10, %,
15%, 20% or more higher than default settings) and/or to turn off audible
alarms sounds. As another example, do not disturb mode is activated by a
button on the receiver's user interface, whereby a user can simply press
the "do not disturb button" and all alarms and therapy calculations turn
off and/or are not activated for a predetermined time period. As another
example, exercise mode sets parameters to ensure the therapy module
calculates appropriate caloric intake suitable during and after exercise.
As another example, mealtime mode sets parameters to ensure the therapy
module calculates appropriate medicament delivery suitable during and
after a meal. As another example, day mode is associated with more
sensitive alarm thresholds and more noticeable alarm types.

[0590] Methods and devices that are suitable for use in conjunction with
aspects of the preferred embodiments are disclosed in U.S. Pat. No.
4,994,167; U.S. Pat. No. 4,757,022; U.S. Pat. No. 6,001,067; U.S. Pat.
No. 6,741,877; U.S. Pat. No. 6,702,857; U.S. Pat. No. 6,558,321; U.S.
Pat. No. 6,931,327; U.S. Pat. No. 6,862,465; U.S. Pat. No. 7,074,307;
U.S. Pat. No. 7,081,195; U.S. Pat. No. 7,108,778; U.S. Pat. No.
7,110,803; U.S. Pat. No. 7,192,450; U.S. Pat. No. 7,226,978; U.S. Pat.
No. 7,310,544; U.S. Pat. No. 7,364,592; U.S. Pat. No. 7,366,556; and U.S.
Pat. No. 7,424,318.

[0593] All references cited herein, including but not limited to published
and unpublished applications, patents, and literature references, are
incorporated herein by reference in their entirety and are hereby made a
part of this specification. To the extent publications and patents or
patent applications incorporated by reference contradict the disclosure
contained in the specification, the specification is intended to
supersede and/or take precedence over any such contradictory material.

[0594] The term "comprising" as used herein is synonymous with
"including," "containing," or "characterized by," and is inclusive or
open-ended and does not exclude additional, unrecited elements or method
steps.

[0595] All numbers expressing quantities of ingredients, reaction
conditions, and so forth used in the specification are to be understood
as being modified in all instances by the term "about." Accordingly,
unless indicated to the contrary, the numerical parameters set forth
herein are approximations that may vary depending upon the desired
properties sought to be obtained. At the very least, and not as an
attempt to limit the application of the doctrine of equivalents to the
scope of any claims in any application claiming priority to the present
application, each numerical parameter should be construed in light of the
number of significant digits and ordinary rounding approaches.

[0596] The above description discloses several methods and materials of
the present invention. This invention is susceptible to modifications in
the methods and materials, as well as alterations in the fabrication
methods and equipment. Such modifications will become apparent to those
skilled in the art from a consideration of this disclosure or practice of
the invention disclosed herein. Consequently, it is not intended that
this invention be limited to the specific embodiments disclosed herein,
but that it cover all modifications and alternatives coming within the
true scope and spirit of the invention.